As a complex nonlinear system, the agricultural economic system has many irregularities and complexities hidden behind its data. Based on this reason, this article proposes a nonlinear dynamic characteristic analysis model of agricultural economic data based on deep neural network (DNN) by using nonlinear dynamics theory. In this study, 20 representative agricultural economic data sample platforms were selected, with the help of data preprocessing and outlier removal. Then, DNN is used to train the processed data, and the optimized network weight is obtained, and the agricultural financial industry assessment model is constructed. The research shows that the evaluation model combined with DNN is superior to the traditional support vector machine (SVM) model in accuracy and mean absolute error (MAE), and its accuracy is over 90%. This result largely proves the excellent effect of DNN in capturing the nonlinear dynamic characteristics of agricultural economic data. Therefore, it can be considered that the method proposed in this paper provides a new idea for the analysis and prediction of agricultural economic data and can promote the healthy development of the agricultural economy.