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In order to suppress the disturbance of auxiliary information in various directions in the recommendation model and consider using text information to extract item features，this paper proposes a matrix factorization model，which is a SVD++ model of hybrid deep collaborative filtering for recommender method (SHD). The model combines additional stacked denoising autoencoder (ASDAE) and stacked contractive denoising autoencoder (CDAE) with auxiliary information to extract latent feather representations of users and items，and adds pre-trained word embedding model to consider the semantic relationship of words when extracting item feature representations. Experimental results on the MovieLens_1M and MovieLens_10M show that compared with traditional algorithms, deep learning algorithms, and variants of the proposed model, the proposed model is more effective in extracting latent feature representations and improving the accuracy of prediction score. [ABSTRACT FROM AUTHOR]