To improve the performance of image classification, this paper proposes an image classification algorithm based on the fusion of Multi-model Feature and Reduced Attention (MFRA). Through multi-model feature fusion, the network can learn the features of different levels of input images, increase the complementarity of features and improve the ability of feature extraction. The introduction of the attention module makes the network pay more attention to the target area and reduces the irrelevant background interference information. In this paper, the effectiveness of the algorithm is verified by a large number of experimental comparisons on three public datasets, Cifar-10, Cifar-100 and Caltech-101. The classification performance of the proposed algorithm is significantly improved as compared with existing algorithms.