Entity recognition algorithm and transaction characteristics analysis of bitcoin blockchain

Abstract

At present, blockchain is in a period of rapid development. However, there are many hidden dangers in its development process. This paper completed the analysis and verification after studying various indicators of the Bitcoin network and putting forward hypotheses based on experience. It is found that the correlation between bitcoin network trading volume and the amount of account funds remains stable. Most of the early saving accounts are actually still transaction participants in the current ecosystem. At present, most bitcoin transaction network entity recognition and classification methods are still based on network graph structure features. This paper proposed an entity recognition and classification method based on the relevance features by starting from the characteristics of bitcoin’s own multi-transaction association. Some data are obtained from public websites, and it shows that the method in experiments could improve the performance of entity recognition effectively. In addition, this paper also analyzes the importance of features and the relevance of classification. In order to deal with the above transaction network feature analysis and entity recognition of the actual demand, this paper also designed a set of highly automated, timely interruption and recovery, and abnormal alarm function of the system. It can be migrated to other Bitcoin networks, and it is very convenient and efficient. This paper can improve the performance of entity recognition and make our understanding of Bitcoin deeper. This system has great theoretical value and application value for the research of bitcoin de-anonymization.