In order to evaluate the image quality better and solve the problem of the differences between the block images that are obviously ignored in the image quality evaluation model based on the convolutional neural network (CNN-IQA), a CNN model of multi-feature fusion is proposed. Firstly, the whole image is divided into non-overlapping blocks, the information entropy and texture feature of each divided image are extracted. Then, the two features are combined to calculate the importance weight of each block image to measure the influence of the block image on the quality of the distorted image. Finally, the loss function is modified according to the calculated importance weight to highlight the role of the block image with high importance in the training process. Validation and comparison experiments on the LIVE data set found that the SROCC and LCC indicators of the algorithm are 0962 and 0.960, which are higher than the original algorithm at least 0.9%. The validation and comparison experiments on the TID2008 data set show that the SROCC and LCC indicators obtained by the algorithm are 0.922 and 0.924, which are higher than the original algorithm at least 0.6%. And the results on the two data sets are better than other comparison algorithms. The experimental results prove that it has good performance and generalization in predicting image quality.