The traditional wood defect location methods mainly include physical equipment detection and traditional computer technology detection, but they are difficult to collect data, highly dependent on the data itself, which are not suitable for actual production. We propose an automatic defect location model (ADLM) based on deep learning, which includes single defect location model (SDLM) and multi-defect location model (MDLM) to meet different requirements. This model uses MobileNet as the backbone network, and only a few data sets are needed for training. In the public data set Wood Defect Database, this model has a defect identification rate of 86.1%.In the single defect data set, positioning accuracy of the model can achieve 97.5%. In the multi-defect data set, positioning accuracy of the model can achieve 90.0%. Compared with the traditional model, the ADLM need not manual feature extraction at the early stage, and has the advantages of faster detection speed, higher accuracy and wider applicability.