In order to improve the efficiency of circuit breaker life prediction and formulate a reasonable maintenance plan, an IFMD-BiTCN-BiGRU-AT prediction model based on crown porcupine optimization algorithm (CPO) is proposed based on the characteristics that the non-periodic vibration signal of the circuit breaker can fully characterize the residual life. Firstly, the feature mode decomposition method is improved by integrating the fitness function and the new period estimation method to make up for its poor ability to deal with non-periodic signals, and the IFMD adaptive decomposition is realized by using CPO. Secondly, a two-way parallel structure and attention mechanism are introduced. The BiTCN-BiGRU-AT prediction model is constructed to fully extract the important features of time-space, and the CPO is used to search the optimal hyperparameter combination. Finally, the experimental platform of circuit breaker signal acquisition and processing is built for experimental verification. The method is used to predict and design ablation experiments and multi-model comparison experiments. Finally, the fitting degree, MAE and RMSE indexes obtained by this method are 99.28%, 80.33 and 98.17 respectively. Compared with the other three signal processing methods, the prediction fitting degree is increased by 19.7% on average after IFMD processing, and the prediction efficiency is the highest. Compared with other models, the prediction fitting degree of the model is increased by 18.3% on average, and the MAE and RMSE are reduced by 60.9% and 61.6% on average. Experimental results show the effectiveness and performance advantages of the proposed method.