A data classification algorithm for ideological and political teaching resources integrating density peak clustering and random forest

In this paper, a data classification algorithm for ideological and political teaching resources that integrates density peak clustering and random forest is proposed. Firstly, process missing and outlier values through data cleaning steps to ensure data quality. Next, using correlation analysis and information gain based methods, important features for classification are selected to reduce the dimensionality of the data. Subsequently, the density peak clustering method was used to automatically discover the clustering structure in the data through adaptive distance measurement and local density calculation. Finally, the random forest algorithm is used for classification, and through a dynamic weighted voting mechanism, the voting weight of each decision tree is determined based on its classification accuracy on the validation set to optimise the classification performance. The experimental results show that this method effectively improves the efficiency and accuracy of data classification.