Classification Learning Model of Environmental Protection Data Based on Intelligent Optimization Algorithm

Abstract

Environmental issues are becoming increasingly prominent, and people are paying more and more attention to environmental protection. Intelligent optimization algorithms have good global search capabilities, high accuracy, and real-time performance. Therefore, with the help of intelligent optimization algorithms, this paper established a classification learning model for environmental protection data, and analyzed the data from aspects such as water, solid waste, ecological governance, and ESG. The accuracy of the data obtained by the intelligent algorithm in the analysis results is less than 82%. However, it can still improve the processing capacity of relevant environmental data. This article mainly uses experimental comparison and algorithm analysis methods to analyze the results of environmental protection data classification learning. At the same time, the accuracy and stability of the entire algorithm were tested, and the accuracy of the average K, LWF, and IAE during the incremental learning process of MNIST data was compared. Experimental data show that the data accuracy rate obtained by the improved intelligent optimization algorithm exceeds 85%. Therefore, in data mining, existing environmental protection data can be used for statistical analysis and prediction of these classification objects.