Research on Enterprise Workflow Engine Optimization based on Knowledge Graph Algorithm

In this paper, an intelligent solution based on knowledge graph and graph theory is proposed to solve the problem of data islanding and inefficiency of cross-system linkage caused by the difference of heterogeneous system protocols in the process of enterprise informatization. Through the construction of root-parent-child three-layer model architecture, the standardized representation of heterogeneous protocols such as RESTful is realized, and the inheritance mechanism is used to unify data formats and interface specifications (such as HTTP method and JSON data format), which solves the automation problem of protocol parsing. It innovatively abstracts business logic into five types of intelligent operators (trigger, connect, transform, logic, output), constructs directed acyclic graph by visual drag-and-drop arrangement, and combines Kahn algorithm to realize topological sorting and workflow scheduling, so that business personnel can complete complex scene configuration without coding. At the same time, the workflow knowledge graph is generated automatically based on graph theory technology, and the execution path is optimized by its reasoning ability to realize anomaly monitoring and dynamic adjustment. The empirical results show that this method can reduce the cross-system development cost by 80%, improve the process arrangement efficiency by 60%, and shorten the problem location time by 75% through knowledge kinship tracing. The research results provide an expandable technical framework for enterprises to break information silos and realize intelligent cross-system collaboration, which has significant engineering application value.