Abstract:  In order to quickly extract key information from massive library knowledge graphs, an entity relationship extraction algorithm for academic text keyword library knowledge graphs is proposed. OCS-FCM (Optimization of Complete Strategy Fuzzy C-Means) and Elastic E-t-SNE(Embedding t-Distributed Stochastic Neighbor Embedding ) algorithms are used to perform missing value filling and dimensionality reduction on key words in the library. And using entities in the academic text keyword library as vertices, a knowledge graph is established. Based on the part of speech and other features of keywords, a SelfATT BLSTM(Self Attention Bidirectional Long Short Term Memory) model is constructed using a self attention mechanism algorithm to extract entity relationships from the knowledge graph and obtain the extracted results. Experimental results have shown that the collection accuracy of proposed algorithm is more than 0. 8, with an ACC(Accuracy) value over 30% and a extraction time less than 1.5 s, demonstrating excellent ability to extract entity relationships. 

Key words: optimization of complete strategy fuzzy C-Means(OCS-FCM) algorithm, filling in missing data values, knowledge graph, self-attention bidirectional long short-term memory ( SelfATT- BLSTM) model