Laser induced damage to optical thin film components is a bottleneck that limits the development of lasers towards high-power and high-energy. Therefore, rapid detection of optical thin film damage has become an urgent problem to be solved. To improve the accuracy and efficiency of damage identification and classification for optical thin film components, a deep learning based damage image classification model training method is proposed. Collect images of laser irradiated oxide thin film damage, extract feature information such as RGB values, grayscale, texture, shape, etc. of the damaged area through preprocessing such as noise removal and image enhancement, and input BP neural network training for recognition. Due to the limited number of datasets and computational errors, the classification results did not meet the expected values, Therefore, transfer learning was used to train the data set. The results showed that transfer learning was better than BP neural network in terms of accuracy and sensitivity, with an accuracy rate of 90%, The depth transfer learning technology is applied to the damage identification of optical thin film components, which provides a new idea to solve the laser induced damage identification of optical thin films.