In order to deal with the problem of low pedestrian re-identification rate in video images due to small target pedestrians， occlusions and variable pedestrian postures， a dual-channel multi-scale integrated learning method was established based on efficient network EfficientNet. Firstly， EfficientNet-B1 （EfficientNet-Baseline1） network was used as the backbone structure. Secondly， a weighted Bidirectional Feature Pyramid Network （BiFPN） branch was used to integrate the extracted global features at different scales. In order to improve the identification rate of small target pedestrians， the global features with different semantic information were obtained. Thirdly， PCB （Part-based Convolutional Baseline） branch was used to extract deep local features to mine non-significant information of pedestrians and reduce the influence of pedestrian occlusion and posture variability on identification rate. Finally， in the training stage， the pedestrian features extracted by the two branch networks respectively were calculated by the Softmax loss function to obtain different subloss functions， and they were added for joint representation. In the test stage， the global features and deep local features obtained were spliced and fused， and the Euclidean distance was calculated to obtain the pedestrian re-identification matching results. The accuracy of Rank-1 of this method on Market1501 and DukeMTMC-Reid datasets reaches 95.1% and 89.1% respectively， which is 3.9 percentage points and 2.3 percentage points higher than that of the original backbone structure respectively. Experimental results show that the proposed model improves the accuracy of pedestrian re-identification effectively.