## 6 Conclusions  
In this paper, we propose a joint Daen-LR, ARIMA-LSTM, and MLR machine learning algorithm (JMLA). Firstly, we combine a double adaptive elastic network with a traditional logistic model to select variables that have both Oracle and adaptive classification characteristics. These two characteristics eliminate the influence of different categories on the inconsistent selection of important independent variables and the influence of strong-correlation independent variables on the interference of weak independent variables. Secondly, we combine the deep learning model (LSTM) with the ARIMA time series model so that it can handle both linear and nonlinear trends. By calculating the ARIMA-LSTM model, we establish the correlation curve of chemical composition before and after weathering and predict the change in chemical composition with weathering. Thirdly, we combine the data processed by the above two improved methods with the multiple linear regression model to classify the unknown glass relics. The experimental results show that the accuracy of the JMLA model on the train set is 97.9%, and the accuracy of the JMLA model on the test set is 97.6%. In addition, we compared JMLA with similar classifiers, and the results were shown in Train Acc, Test Acc, Precision, Recall, and F Score indexes. The difference values of JJMA's performance over the past optimal algorithm model are +0.017, +0.025, +0.046, +0.035, and +0.040, respectively. These data show that the JMLA model has better performance than other classification models without changing the structure of similar classification models and under the same experimental conditions. The classification accuracy of the JMLA model is higher than other models, especially for large glass relics with more chemical elements and a harsh environment. This processing method is practical and reliable in the direction related to the composition analysis and identification of cultural heritage. The application of this method is expected to improve the accuracy of the classification of cultural relics by archaeologists and can effectively reduce the impact of identification difficulties caused by factors such as harsh burial environments. It helps us to have a deeper understanding of the exchange, penetration, and development of ancient Eastern and Western cultures. In addition, the future research directions of this study can be summarized as follows:  
1. Algorithm optimization. The processing method uses a variety of machine learning algorithms that effectively combine the advantages of each algorithm with high practicality and feasibility and a good fitting effect. However, this model is only combined with an LSTM deep learning neural network, which can be combined with more advanced deep learning models in the future so as to improve the accuracy and efficiency of classification. 2. Reduce model calculation costs and formula complexity. Although the classification accuracy of the JMLA model is very high, the calculation time is relatively long compared with other models, and the formula is relatively complex, which is also a pain point for the JMLA model. Therefore, reducing the calculation amount and better integrating the three models will be the focus of future research. 3. Application prospects of this data processing method. In spite of its application in the direction of heritage composition analysis and identification, it is expected to be applied in the areas of health, food safety, and environmental protection, for example: analysis and classification of chemical constituents of tobacco; composition analysis of nutritional composition in food; classification and monitoring of pollutant composition in air, etc. Conceptualization, Z.-X.L. and P.-S.L. ; data curation, P.-S.L. ; formal analysis, Z.-X.L. ; investigation, J.-H.L. ; methodology, Z.-X.L. ; project administration, Z.-H.Y., Y.-P.M. and H.-H.W. ; resources, G.-Y.W. ; software, P.-S.L. ; supervision, G.-Y.W. and H.-H.W. ; validation, J.-H.L., Z.-H.Y. and [PERSON]-P.M.; visualization, J.-H.L.