Abstract:Using natural language processing methods such as text classification and sentiment analysis, this article develops a regional environmental image evaluation method based on Internet news. In order to meet the analysis needs of big data on ecological environment, the category of environmental image is divided. Regional environmental images are divided into three categories, including news sources, environmental elements, and emotional attitudes. This research constructs an environment-specific corpus and compares three algorithms, including support vector machines, naive Bayes, and convolutional neural networks and finally build a regional environmental image evaluation method with convolutional neural network. The F1 values of the three classifications all meet the analysis needs. The F1 value of environmental elements is between 0.8 and 0.9, the F1 value of sentiment analysis is above 0.8, and the F1 value of stylistic sources is about 0.9. The method is well applied to cities in the Yangtze River Delta region. It can process regional environmental public opinion in real time, analyze the environmental image of the region, and provide intuitive conclusions and basic information support for regional environmental management.