Printed Circuit Board (PCB) is one of the most important components of electronic products. But the traditional defect detection methods are gradually difficult to meet the requirements of PCB defect detection. The research on PCB defect recognition method based on convolutional neural network is the current trend. The PCB defect image recognition based on DenseNet169 network model is studied in this paper. In order to reduce the omission of PCB defects in actual detection, it is necessary to further improve the sensitivity of the model. Therefore, a classification model based on the multimodel fusion of the DenseNet169 model and the ResNet50 model is proposed. At the same time, the network structure after multimodel fusion is improved. The improved multimodel fusion model Mix-Fusion enables the network to not only retain the recognition accuracy of the ResNet50 model for NG defects and small defect images but also improve the overall recognition accuracy through the feature reuse and bypass settings of the DenseNet169 model. The experimental results show that when the threshold is 0.5, the sensitivity of the improved multimodel fusion network can reach 99.2%, and the specificity is 99.5%. The sensitivity of Mix-Fusion is 1.2% higher than that of DenseNet169. High sensitivity means fewer missed NG images, and high specificity means less workload for employees. The improved model improves sensitivity and maintains high specificity.