The rapid growth of Location-Based Social Networks (LBSN) provides a vast amount of Point-of-Interest (POI) data, which facilitates the research of POI recommendation. To solve the low recommendation accuracy caused by the extreme sparseness of user-POI matrix and the lack of POI features, by integrating information such as tags, geography, socialization, score, and image information of POI, a POI recommendation method integrating social networks and image contents called SVPOI was proposed. Firstly, with the analysis of POI dataset, a distance factor was constructed based on power law distribution and a tag factor was constructed based on term frequency, and the existing historical score data was merged to construct a new user-POI matrix. Secondly, VGG16 Deep Convolutional Neural Network (DCNN) was used to process the images of POI to construct the POI image content matrix. Thirdly, the user social matrix was constructed according to the social network information of POI data. Finally, with the use of Probabilistic Matrix Factorization (PMF) model, the POI recommendation list was obtained with the integration of user-POI matrix, image content matrix and user social matrix. On real-world datasets, the accuracy of SVPOI is improved significantly compared to PMF, SoRec (Social Recommendation using probabilistic matrix factorization), TrustMF (Social Collaborative Filtering by Trust) and TrustSVD (Social Collaborative Filtering by Trust with SVD) while Mean Absolute Error (MAE) and Root-Mean-Square Error (RMSE) of SVPOI are decreased by 5.5% and 7.82% respectively compared to those of TrustMF which was the best of the comparison methods. The experimental results demonstrate the recommendation effectiveness of the proposed method.