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Commonly used datasets in the field of recommendation suffer from unbalanced data distribution, sparsity and different user rating preferences. All these problems affect the quality of recommendation. Thus, this paper proposed a recommender model combining hard example mining with adversarial autoencoder. Considering the difference of users’ preference, Mean Model based triplet loss algorithm was introduced to classify the dataset into positive and negative samples and thus improve the quality of the training data. The application of Mean Model can both reduce computational complexity and retain the statistical feature of original data. Using classified samples, the rating prediction model was trained from both reconstruction and adversarial aspects. Adam optimization algorithm was used to calculate different update gradients for different parameters. Experimental results show that the recommendation model improves the recommendation accuracy significantly, and several performance indexes are better than baseline models. Hard example mining based adversarial autoencoder recommender system has certain practical value.