As one of the most widely used federated chains, hyperledger fabric uses many cryptographic algorithms to ensure the security of information on the chain, but the ECDSA cryptographic algorithm used in the fabric system has backdoor security risks. In this paper, the authors adopt SM2 algorithm to replace the corresponding ECDSA algorithm for blockchain design based on fabric platform. Firstly, they optimize the part of SM2 signature algorithm process with inverse operation and effectively reduce the time complexity by reducing the inverse operation in the whole process, and the experimental results show that the improved SM2 algorithm improves the signature and verification efficiency by about 5.7%. Secondly, by adding SM2 algorithm template and interface to the BCCSP module of fabric platform to realize the shift value of SM2 algorithm and compare the performance with the native fabric system, the network startup time is reduced by about 29%. The experimental results show the effectiveness of the improved SM2 algorithm, and also the performance of the optimized fabric system is improved.

Discover the deep semantics from the massively structured data in knowledge graph and provide reasonable explanations are a series of important foundational research issues of artificial intelligence. However, the deep semantics hidden between entities in knowledge graph cannot be well expressed. Moreover, considering many predicates express fuzzy relationships, the existing reasoning methods cannot effectively deal with these fuzzy semantics and interpret the corresponding reasoning process. To counter the above problems, in this article, a new interpretable reasoning schema is proposed by introducing fuzzy theory. The presented method focuses on analyzing the fuzzy semantic between related entities in a knowledge graph. By annotating the fuzzy semantic features of adjacency predicates, a novel semantic reasoning model is designed to realize the fuzzy semantic extension over knowledge graph. The evaluation, based on both visualization and query experiments, shows that this proposal has advantages over the initial knowledge graph and can discover more valid semantic information.

In a big data environment, traditional recommendation methods have limitations such as data sparseness and cold start, etc. In view of the rich semantics, excellent quality, and good structure of knowledge graphs, many researchers have introduced knowledge graphs into the research about recommendation systems, and studied interpretable recommendations based on knowledge graphs. Along this line, this paper proposes a scholar recommendation method based on the high-order propagation of knowledge graph (HoPKG), which analyzes the high-order semantic information in the knowledge graph, and generates richer entity representations to obtain users’ potential interest by distinguishing the importance of different entities. On this basis, a dual aggregation method of high-order propagation is proposed to enable entity information to be propagated more effectively. Through experimental analysis, compared with some baselines, such as Ripplenet, RKGE and CKE, our method has certain advantages in the evaluation indicators AUC and F1.