Ensuring the safe operation of power cables is the basis of building a new intelligent power system. In order to realize the digital early warning of external force damage events, an online identification method of external force damage vibration signals based on VMD-WOA-ELM is proposed. Firstly, the collected abnormal vibration signal is decomposed into several intrinsic modulus function components (IMF) by VMD, then the time and frequency domain eigenvalues of each IMF component are extracted to form the eigenvector, and finally the extreme learning machine (ELM) is used to identify the type of vibration signal. In order to solve the problem of poor classification stability caused by the random selection of initial weights and thresholds of ELM model, whale optimization algorithm (WOA) is used to optimize the parameters of ELM to obtain the optimal classification model. This method is applied to the identification experiment of construction vibration signal type. The vibration signals of four typical breaking events were collected, and each signal has 100 groups. 80% of them were used as the training set and 20% as the test set to test the recognition performance of the algorithm. The algorithm is compared with traditional ELM, PSO-ELM and GA-ELM. The results show that under the same computer operating conditions, the classification accuracy of WOA-ELM is 98.75%, which is 5% higher than that of traditional ELM, and the overall running time is only 4.10 s. Compared with the other two algorithms, this algorithm has the highest recognition accuracy, the fastest convergence speed and the best comprehensive performance.