Research on the Optimization of Digital Economy Platform Service Models Based on Artificial Intelligence and Blockchain

With the rapid development of the digital economy, the efficiency, security and transparency of the platform have become increasingly important. Traditional platforms often encounter some problems when processing large amounts of user data, such as data insecurity, information asymmetry, and slow service response. To solve these problems, we proposed a new service model optimization method that combines cluster analysis with blockchain technology to improve the platform's operating efficiency and service quality. In the study, we used several common clustering algorithms, such as K-means and hierarchical clustering, to analyze user behavior data on digital platforms and find different user behavior patterns. At the same time, we also introduced blockchain technology to ensure that data can be stored and managed securely. Through its decentralized mechanism and smart contracts, we automated some processes and reduced manual operations. In the experimental phase, we collected 3,000 user behavior data from China's JD platform. Through cluster analysis, we divided users into several different groups based on their behavioral characteristics, and then combined blockchain technology to optimize the service processes of these user groups. The effect of this combination strategy is very obvious. The experimental results show that cluster analysis can effectively identify the differentiated needs of different user groups, and blockchain technology has brought significant improvements in data security, transparency and overall service efficiency. However, it should be said that the system will become a bit complicated after the introduction of blockchain.