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Evaluation is an essential part of the teaching process, especially in the programming course. Both students and teachers can benefit significantly from automatic program evaluation. It shortens the time required for assessment so that students can get immediate feedback. At the same time, it can also significantly reduce the workload of teachers. Currently, the automated program assessment system mainly uses a combination of static and dynamic analysis methods. The system is faced with two crucial problems of the unfinished code evaluation and the template code construction. This paper proposes a method of combining deep learning with static analysis. The syntax tree repair is used to solve the problem that the code with compiling errors cannot generate the correct syntax tree. Moreover, the target code is converted to a subset of solution space through the syntax tree standardization, which reduces the number of template code needed. Based on deep learning, the embedded token vector keeps the code' s context all the time, which ensures that the lexical-semantic remains unchanged as much as possible after the syntax tree changes. Finally, the standardized tree is represented as a vector by the recursive neural network. Cosine similarity between target and template code vectors is used as an evaluation score. The experiment shows that the similarity scores obtained by this method are consistent with the expert scores. This method can provide support for future research, such as difficult feedback and has great significance.