## 2 Research Theory and Methods
### Mining Technology under Big Data  
DM must first be used to preprocess the data. This necessitates some additional processing of the acquired data before the big data technology processing [42]. The main purpose of DM is to provide more accurate data that meets the requirements and improves the efficiency and accuracy of information processing by big data technology. This operation is one of the important information processing aspects of big data technology. There are various methods for data preprocessing, including data integration, cleaning, selection, etc. [43]. With the support of these data preprocessing technologies, the synthetical quality  
Figure 2: The design approach for industry–education cooperation. of DM technology in practical applications is greatly improved, the time required for actual DM is reduced, and the practicability of actual data acquisition is improved. Data cleaning is the integrated improvement in incomplete, noisy, and inconsistent data in the real world. Namely, through data filling, noise reduction, and prediction methods, the data are cleaned to improve their integrity and practicability [44]. The requirements of the results of data cleaning comprise the standardization of data format, ensuring no data are abnormal, removing erroneous data, and filling f blank data. Data integration is the unified processing of multiple data sources in massive data sets to improve the effectiveness of data storage [45]. If the form of the original data is not suitable for the information processing algorithm, data transformation is necessary. Data integration involves combining data from multiple data sources into a consistent store. Data selection involves filtering and selecting the data to be used. Big data technology mainly includes the K-nearest neighbors (KNN), naive Bayes, k-means, apriori, and PageRank algorithms, and SVM. In this study, we principally designed and optimized the SVM algorithm to enable its application in an intelligent management platform for industry-education cooperation, to improve the efficiency of the platform DM, and promote its development [46]. The VSM algorithm is an example of a class of machine algorithms that perform generalized linear classification of data by supervised learning, and its decision boundary is the maximum margin hyperplane that solves the learning samples. Therefore, its main core is a linear classification algorithm, and its input data and learning objectives are:  
\[\mathbf{X}=\{x_{1},\ldots,x_{N}\} \tag{1}\]  
\[\mathbf{y}=\{y_{1},\ldots,y_{N}\} \tag{2}\]  
where \(\mathbf{X}\) represents the input data, and \(y\) is the learning objective. If there is a hyperplane as the decision boundary in the feature space where the input data are located, the learning targets are separated into positive and negative classes. If the point-to-plane distance of any sample is greater than or equal to one, then the calculation for the decision boundary and the point-to-plane distance is as follows:  
\[\mathbf{w}^{\top}\mathbf{X}+b=0 \tag{3}\]  
\[y_{i}\Big{(}\mathbf{w}^{\top}\mathbf{X}_{i}+b\Big{)}\geq 1 \tag{4}\]  
where \(\mathbf{w}^{\top}\) denotes the normal vector of the hyperplane, and \(b\) indicates the intercept of the hyperplane. Then, the hyperplane is used as the interval boundary to discriminate the sample classification algorithm, as exhibited in Equations (5) and (6):  
\[\mathbf{w}^{\top}\mathbf{X}_{i}+b\geq+1,\Rightarrow y_{i}=+1 \tag{5}\]  
\[\mathbf{w}^{\top}\mathbf{X}_{i}+b\leq-1,\Rightarrow y_{i}=-1 \tag{6}\]  
The implication is that all samples above the upper interval boundary belong to the positive class, and those below the lower interval boundary belong to the negative class. The calculation of the loss function of the SVM algorithm is expressed in Equation (7):  
\[L(p)= \begin{cases}0&p<0\\ 1&p\geq 0\end{cases}  \tag{7}\]  
where \(p\) is probability. The 0-1 loss function is not a continuous function, so is not conducive to the solution of optimization problems. So, the usual choice is to construct a proxy loss.