Modern intelligent transport systems focus on the integration of multiple sensors to obtain hybrid navigation schemes. A key issue of a hybrid scheme is distribution of the information sharing coefficients (ISCs) of subsystems and the fusion of parallel multiple observations of navigation sensors. Recently, deep learning methods, particularly convolutional neural networks (CNNs), have achieved great success in image processing tasks. However, there has been limited work in using deep learning for multisensor-based integrated navigation solutions. In this letter, we propose an ensemble learner-based classification and information fusion method, in which estimation error covariance matrices provided by local adaptive filters are used as input for the classifier, and the triple numbers of ISCs are determined by the proposed scheme. The results validate the effectiveness of the proposed scheme, in which the adequately trained ensemble learner can detect the degradation of a subsystem that may suffer atypical observations or faults and consequently can adjust the corresponding ISC in real time.