Evaluation Model of New Socialist Countryside Construction Based on a Multilayer Neural Network in a Complex Environment.

Abstract

The creation of a deep neural network model with numerous hidden layers enables the layer-by-layer extraction of features from the input high-dimensional data, enabling the identification of the data's low-dimensional nested structure and the development of a more efficient and abstract high-level representation. The research on deep learning is thoroughly examined, along with the direction it needs to take going forward. Supporting the construction of new socialist rural areas as a calculated move to address the problems facing farming, rural communities, and farmers is another essential step in furthering modernization. It helps to reshape the entire rural landscape, coordinate the growth of urban and rural areas, achieve the goal of a wealthy society in every way, boost demand, and support the holistic development of people. The emergence of new socialist rural communities against the background of deep learning is the subject of significant research and analysis in this work. In the research, it is examined and studied using deep learning methods and convolutional neural networks. It is evident from the research described in this paper that deep learning backgrounds have a considerable impact on the development of new rural areas—up to 54.53%. In this essay, the foundation for the future construction of a new socialist countryside is laid forth.