The invention discloses a distributed optical fiber vibration signal feature extraction and identification method, which belongs to the field of optical fiber sensing signal processing, and comprisesthe following steps of: firstly, acquiring a space-time matrix signal of a vibration source, extracting a space column signal, dividing a short-time signal unit, and constructing an optical cable vibration event data set; constructing, training and optimizing an improved mCNN model, and performing feature evaluation on features extracted by the model during optimization until model iteration is optimal; secondly, extracting time structure feature vectors under multiple scales in parallel by utilizing an optimal mCNN model, recombining the time structure feature vectors into a short-time feature sequence according to a time sequence, and constructing a time structure feature sequence set; finally, constructing and training an HMM model, and constructing an offline vibration event HMM modellibrary to serve as a classifier for vibration source recognition. The problems that in the prior art, local structure features and time sequence features of distributed optical fiber vibration signals cannot be extracted at the same time, and the vibration source recognition accuracy and the generalization ability of the model are low are solved.