Abstract: An intelligent identification algorithm for effective identification of prohibited items can help reduce the labor intensity of security personnel and improve the efficiency of passenger luggage security. We propose a deep convolutional neural network with multi-label image classification in which attention mechanism and dynamic meta-fusion architecture are adopted to complement low-level image cues during the forward progression of the convolution computing and can effectively cope with the interference of pixel aliasing and the confusion of low-resolution features in fine-grained X-ray image, thus enhancing the ability to recognize fine-grained features. Besides, the meta selection network guided by external neural knowledge is also adopted to achieve adaptive fusion of multi-stage prediction without weight bias. The experimental results show that the proposed algorithm can overcome the difficulty of identification of prohibited items caused by image aliasing and item scale variation in X-ray baggage images, and effectively improve the recognition accuracy.