The introduction of logistics theory and logistics technology has made the government and enterprises gradually realize that the development of logistics has an important strategic role, which can effectively solve the changing needs of users, optimize resource allocation, improve the investment environment, and enhance the overall strength and overall competitiveness of the regional economy. This paper carries out matrix-vector multiplication operations and weight update operations, designs a perceptron neural network model, and realizes a simulation platform based on MLP neural network. Moreover, on the basis of the standard MLP neural network, this paper proposes to use the deep learning training mechanism to improve the MLP neural network, which provides effective technical support for the improvement of the prediction model. In addition, through the fusion of deep learning and MLP neural network, an MLP neural network with three hidden layers is determined. Finally, this paper builds a model based on the MLP neural network algorithm, selects the RBF kernel function as the kernel function of the model by referring to the relevant literature, and uses PSO to optimize the combination of parameters. It can be seen from the result of the evaluation index that each evaluation index is relatively small. The result shows that the prediction is accurate, and the empirical result shows the feasibility of the model to predict the demand for industrial logistics in Shanxi Province.

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Due to the natural production properties, agriculture has been adversely affected by global warming. As an important link between individual household farmers and modern agriculture, it is crucial to study the influence of agricultural productive services on farmers’ climate-responsive behaviors to promote sustainable development and improve agricultural production. In this paper, a questionnaire survey has been conducted among 374 maize farmers by using the combination of typical sampling and random sampling in Jilin Province of China. Moreover, the Poisson regression and the multi-variate Probit model have been used to analyze the effects of agricultural productive services on the choices of climate-responsive behaviors as well as the intensity of the behaviors. The results have shown that the switch to suitable varieties according to the frost-free period have been mostly common among maize growers in Jilin province. Agricultural productive services have a significant effect on the adoption intensity of climate- responsive behaviors, at the 1% level. Based on this conclusion, this paper proposes policy recommendations for establishing a sound agricultural social service system and strengthening the support for agricultural productive services. It has certain reference significance for avoiding climate risk and reducing agricultural pollution in regions with similar production characteristics worldwide.