To address the problems of the expensive computing cost for the wave field data processing and the difficulty for damage feature extraction in laser ultrasonic detection, a guided wave field analysis method based on deep learning is proposed. First, under the framework of VGG-Net, a residual network based on VGG11 is developed for extracting guided wave features from time-space wave field data. Then, taking the local wavenumber characteristic as the physical mechanism of the model, the problem of obtaining big data for training deep learning model can be solved by using the analytic formula of guided wave propagation. Therefore, the neural network can be obtained for extracting guided wave feature. Finally, using the experimental data in the plate structure with damage through laser ultrasonic system as test samples, the capability of guided wave feature extraction and damage identification using the proposed method is validated. The damage identification accuracy is above 67% and the shape of structural damage can be visualized.