The collaborative filtering algorithm has been widely used in the recommendation system. It has significant effects in implementing the new recommendation function, but there are still problems such as sparse data, poor scalability, cold start, etc. New design ideas and technical methods are needed for optimization. In recent years, deep learning has achieved outstanding results in the fields of image processing, target recognition, and natural language processing. Combining the deep neural network model with the recommendation algorithm has brought a new opportunity for the construction of a new recommendation system. In this study, a new hybrid neural network model is proposed, which consists of stack denoising autoencoder and deep neural network. It learns the potential feature vectors of users and projects and the interaction behavior model between users and projects, effectively solves data sparseness, and thus improves the quality of system recommendations. The recommended algorithm model is tested by the MovieLens film scoring data set. The experimental results are compared with traditional recommendation algorithms such as SVD, PMF, and classical autoencoder model algorithms, the recommendation quality is significantly improved.