With the development of Convolutional Neural Network, the classification on ordinary natural images has made remarkable progress by using single feature maps. However, it is difficult to always produce good results on coronary artery angiograms because there is a lot of photographing noise and small class gaps between the classification targets on angiograms. In this paper, we propose a new network to enhance the richness and relevance of features in the training process by using multiple convolutions with different kernel sizes, which can improve the final classification result. Our network has a strong generalization ability, that is, it can perform a variety of classification tasks on angiograms better. Compared with some state-of-the-art image classification networks, the classification recall increases by 30.5% and precision increases by 19.1% in the best results of our network.