Modeling of the ecological economic activity based on machine learning

The eco-economic activity modeling is an effective method to analyze the eco-economic system. From the existing models, it can be seen that the disadvantages of eco-economic activity modeling are that the model evaluation accuracy is not high, and the system stability is poor. In order to improve the evaluation effect of the ecological economic activity, based on the machine learning algorithm, this study establishes a PNN evaluation model based on the probabilistic neural network classification principle. Moreover, in this study, a certain number of learning samples are generated by random interpolation of evaluation index standards, and then Matlab software is used to simulate the training and test of the model, and the feasibility and effectiveness of the model are verified by statistical indicators. In addition, this study combines the actual case to analyze the performance of the model and analyze the test results by statistical analysis methods. The research results show that the model proposed in this study has certain effects and high stability.