Abstract: In order to classify multi-label images more effectively,an improved convolution neural network model is proposed.The model learns multi-scale features in multi-label images by fusing multi-level features and utilizing spatial pyramid pooling.At the same time,an adversarial network is designed to generate new samples to assist model training.Firstly,the traditional convolution neural network model is improved,and the last layer of the network is replaced with the spatial pyramid pooling layer.In addition,the pre-trained parameters on ImageNet are transfered to the model.Then,the deep and shallow features are fused so that the model can acquires better recognition ability for multi-scale objects.Finally,an adversarial network is designed to generate samples with occlusion,therefore the model is also robust to recognize objects with occlusion.Experiments are carried out on two benchmark datasets.The average precision and recall of the proposed model on Corel5K dataset are 0.457 and 0.427,respectively.The mAP value on Corel5K dataset attains 0.442,while the mAP value on PASCAL VOC 2012 dataset attains 0.85.The experimental results show that the proposed model has better effectiveness and stronger robustness than many state-of-the-art models.