Aiming at the problem of difficulty in feature extraction and identification of wake echo signals detected by underwater lidar due to instability, an underwater bubbles recognition algorithm based on PCA feature extraction and elastic BP neural network was proposed. First, slice preprocessing was carried out on the echo signals collected continuously. Then the PCA algorithm was used to extract the main features of the spliced high-dimensional samples to determine the number of feature values. Then the parameters of the elastic BP neural network was selected to determine the number of hidden layer node and the number of features that can achieve optimal classification. Finally, an indoor wake detection simulation platform was used to realize the identification of bubbles and interfering targets. The experimental results show that when the hidden node is 12, the increment factor is 1.15, and the decrement factor is 0.55, two eigenvalues can be selected to classify the bubbles, non-bubbles and interfering targets. The recognition rate increases by 13.4% with the increase of bubbles density. At low density, the average recognition rate increases by 6.3% with the increase of laser energy. The recognition rate first increases and then decreases with the increase of distance. When the bubbles distance is 2.2 m, the target peak characteristics are obvious, and the average recognition rate is improved by 3.5%. Compared with adaptive and additional momentum BP, this method can reduce recognition time and achieve 99.1% accuracy. It is proved that this algorithm can be effectively and widely used in the recognition of bubbles in the ships wake by lidar.