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The continuation of human civilization is inseparable from the development and construction of rural areas, and infrastructure is the core of rural development. China has been building large-scale rural infrastructure in recent years. Rural infrastructure building, for example, is huge in both quantity and scope, but it is beset by challenges in its current construction and development, and it urgently requires suitable leadership. Planning assessment, as a technical method, can identify problems in regional development and is a powerful tool for evaluating the impact of planning and construction and promoting the development of complete new areas. This paper is aimed at the planning evaluation of rural construction and the evaluation of rural construction and guides the planning and implementation of the next step of rural construction, to assist China's supervision and inspection of rural construction effect and promote rural construction and development into a good track. In view of the low accuracy and efficiency of the current evaluation model of rural planning and the problem that a single neural network easily produces local extreme value, the neural network method is improved, and the application of LM-BP neural network in the evaluation model of rural planning is proposed. Input sample elements are five factors affecting rural construction, including industrial construction, population distribution, and utilization rate of large-scale facilities, construction of public facilities, and promotion effect of supporting policies. Output sample is the evaluation result. On this foundation, the LM-BP neural network was used to convert the training into a least square problem, and the LM method was used to redefine the number of hidden layer nodes, resulting in the construction of a rural planning evaluation model based on the LM-BP neural network. This approach is used to determine the outcomes of rural planning evaluations. The experimental results show that the designed evaluation model has a small evaluation error, has the advantage of high accuracy compared with similar models, and is a reliable evaluation model for rural planning. [ABSTRACT FROM AUTHOR]