Identity-recognition technologies require assistive equipment, whereas they are poor in recognition accuracy and expensive. To overcome this deficiency, this paper proposes several gait feature identification algorithms. First, in combination with the collected gait information of individuals from triaxial accelerometers on smartphones, the collected information is preprocessed, and multimodal fusion is used with the existing standard datasets to yield a multimodal synthetic dataset; then, with the multimodal characteristics of the collected biological gait information, a Convolutional Neural Network based Gait Recognition (CNN-GR) model and the related scheme for the multimodal features are developed; at last, regarding the proposed CNN-GR model and scheme, a unimodal gait feature identity single-gait feature identification algorithm and a multimodal gait feature fusion identity multimodal gait information algorithm are proposed. Experimental results show that the proposed algorithms perform well in recognition accuracy, the confusion matrix, and the kappa statistic, and they have better recognition scores and robustness than the compared algorithms; thus, the proposed algorithm has prominent promise in practice.