The differences between different protocols are not considered when selecting identification features in the existing statistical feature-based identification methods. In order to solve the problem, a Semi-supervised Subspace-clustering Protocol Identification Approach (SSPIA) was proposed by combining semi-supervised learning and Fuzzy Subspace Clustering (FSC) method. Firstly, the prior constraint condition was obtained by transforming the labeled sample flow into pairwise constraints information. Secondly, the Semi-supervised Fuzzy Subspace Clustering (SFSC) algorithm was proposed on this basis and was used to guide the process of subspace clustering by using the constraint condition. Then, the mapping between class clusters and protocol types was established to obtain the weight coefficient of each protocol feature, and an individualized cryptographic protocol feature library was constructed for subsequent protocol identification. Finally, the clustering effect and identification effect experiments of five typical cryptographic protocols were carried out. Experimental results show that, compared with the traditional K-means method and FSC method, the proposed SSPIA has better clustering effect, and the protocol identification classifier constructed by SSPIA is more accurate, has higher protocol identification rate and lower error identification rate. The proposed SSPIA improves the identification effect based on statistical features.