The application discloses a function
programming intelligent recommendation method and
system based on a semantic
knowledge graph, relates to the technical field of
data processing, extracts
semantic feature vectors from multi-
source function programming corpus, constructs a high-dimensional semantic embedding space, and uses a semantic aggregation pre-screening mechanism to preliminarily cluster embedding vectors to obtain each candidate entity cluster and perform dynamic
processing. Then, according to the neighborhood distribution of the semantic embedding space, the
semantic relationship between each candidate entity cluster is constructed, the inter-cluster distribution is adaptively corrected, and a semantic
knowledge graph is generated to form a recommendation method. From corpus to
knowledge graph to recommendation strategy, a
closed loop is formed, so that the intelligent recommendation
system can accurately identify function semantic relationships, reduce
noise interference, improve recommendation
hit rate and personalized matching effect, thereby significantly enhancing the accuracy, stability and
user satisfaction of the function
programming intelligent recommendation based on the semantic knowledge graph.