The large sample and high-dimensional condition monitoring data has technical challenges for accurate prediction of remaining useful life (RUL). A two-stage selective deep neural network ensemble method based on high-dimensional data was proposed to improve the prediction accuracy and convergence efficiency of complex equipments represented by aviation turbofan engine. The first stage was the candidate set generation scheme under the joint disturbance of multiple methods. By using heterogeneous neural network structure, multi time scale design and algorithm parameter randomization to eliminate the internal coupling relationship of the model, the diversity of candidate deep neural network sets was enhanced. In the second stage, the genetic algorithm was used to integrate pruning redundant models. The method effectively the eliminated redundant learners with poor performance to obtain the diversified optimal candidate subsets and output the prediction results according to the average integration. The final result was estimated by average. The experimental comparison with the data of the individual model shows that the proposed method enhances the accuracy and diversity of the ensemble model and improves the RUL prediction result by nearly 20%, which provides stable support for maintenance decision.