Research on the Construction Method of Intelligent Services Based on Prior Knowledge

Introduction

The current service computing paradigm still faces bottlenecks in terms of insufficient intelligence in understanding user needs, managing service resources, and accurately matching needs with services. To overcome the above limitations, this study proposes an intelligent service computing framework driven by prior knowledge, aiming to build an end-to-end intelligent service link, thereby essentially improving the intelligence level of service computing, operational efficiency and user personalized experience. This framework is based on large-scale historical user demand data and domain knowledge, and systematically develops the following four mutually supporting research directions: (1) Intent recognition methods based on prior knowledge, which are committed to improving the accuracy, generalization performance and model interpretability of intent recognition; (2) Demand intention end construction methods based on intent recognition, which aim to build a high-quality demand intention end and achieve accurate perception of complex user demand situations and contextual intent representation; (3) Service end construction methods based on prior knowledge, which focus on building an intelligent service end to achieve automated and dynamic management of service resources and effective mining of highly reusable business models; (4) Demand intention-service matching end construction methods based on prior knowledge, which aim to build an accurate, personalized and interpretable matching end to achieve efficient coordination between user needs and service capabilities. The core innovation of this research lies in deeply integrating artificial intelligence technology into the entire process of service computing, organically integrating cutting-edge technologies such as knowledge graphs, natural language processing, and machine learning, and constructing a systematic knowledge-driven methodology. It is expected that the research results will significantly improve the accuracy of service recommendations, operational efficiency and user satisfaction, promote the evolution of the service computing field towards higher-level intelligence, efficiency and personalization, and lay the theoretical and technical foundation for building the next-generation intelligent service ecosystem.