Aimed at the problems that the degradation parameters monitored by aviation engine sensors are difficult to be extracted, is subject to noise interference, and accuracy is insufficient in predicting engine remaining useful Life (RUL), a new remaining useful life prediction model is proposed by utilizing the maximal information coefficient (MIC), bayesian optimization (BO) algorithm, and categorical boosting(CatBoost) algorithm. Firstly, to effectively address the issue of inadequate feature extraction, the collected historical monitoring features of sensors are subjected to maximal information coefficient correlation calculation to extract key degradation features from the significant impact on the engine's operational lifespan.Secondly, to address the gradient bias and prediction offset issues in remaining useful life prediction,the categorical boosting algorithm method based on bayesian optimization is employed to predict the remaining useful life of aero engines. Finally, experiments are conducted on the commercial modular aero-propulsion system simulation (C-MAPSS) dataset provided by national aeronautics and space administration (NASA). The results show that the proposed prediction method has a good performance, and is valid.