Aiming at solving the problem of insufficient training of Convolutional Neural Network (CNN) acoustic modeling parameters under the low-resource training data condition in speech recognition tasks, a method for improving CNN acoustic modeling performance in low-resource speech recognition was proposed by utilizing multi-stream features. Firstly, in order to make use of enough acoustic information of features from limited data to build acoustic model, multiple features of low-resource data were extracted from training data. Secondly, convolutional subnetworks were built for each type of features to form a parallel structure, and to regularize distributions of multiple features. Then, some fully connected layers were added above the parallel convolutional subnetworks to incorporate multi-stream features, and to form a new CNN acoustic model. Finally, a low-resource speech recognition system was built based on this acoustic model. Experimental results show that parallel convolutional subnetworks normalize different feature spaces more similar, and it gains 3.27% and 2.08% recognition accuracy improvement respectively compared with traditional multi-feature splicing training approach and baseline CNN system. Furthermore, when multilingual training is introduced, the proposed method is still applicable, and the recognition accuracy is improved by 5.73% and 4.57% respectively.