The increasing drug resistance of bacteria，as well as the long cycle of current drug resistance testing methods，bring great challenges and difficulties to accurate drug use at the first time in clinic.Therefore，this paper will explore the application of deep learning technology in the prediction of antimicrobial resistance，and proposes a dual-channel convolution neural network model integrating attention mechanisms. Through the upper and lower channels，different granularity features are extracted from laboratory data after modeling. After convolution and pooling，an attention mechanism is introduced in each channel to focus on important feature information，and then the features of the two channels are fused to complete the classification output. The model is applied to the historical data set of bacterial drug sensitivity test in a tertiary hospital，and compared with other methods.The results show that the proposed method achieves an average improvement of 20.35% in F-value index of classification accuracy，and performs better in small sample classification.