The Automated Essay Scoring (AES) technology can automatically analyze and score the essay, and has become one of the hot research problems in the application of natural language processing technology in the education field. Aiming at the current AES methods that separate deep and shallow semantic features, and ignore the impact of multi-level semantic fusion on essay scoring, a neural network model based on Multi-Level Semantic Features (MLSF) was proposed for AES. Firstly, Convolutional Neural Network (CNN) was used to capture local semantic features, and the hybrid neural network was used to capture global semantic features, so that the essay semantic features were obtained from a deep level. Secondly, the feature of the topic layer was obtained by using the essay topic vector of text level. At the same time, aiming at the grammatical errors and language richness features that are difficult to mine by deep learning model, a small number of artificial features were constructed to obtain the linguistic features of the essay from the shallow level. Finally, the essay was automatically scored through the feature fusion. Experimental results show that the proposed model improves the performance significantly on all subsets of the public dataset of the Kaggle ASAP (Automated Student Assessment Prize) champion, with the average Quadratic Weighted Kappa (QWK) of 79.17%, validating the effectiveness of the model in the AES tasks.