Abstract: 【Purpose/significance】Extracting algorithm terms from massive paper metadata and constructing the innovation evolution re⁃lationship between them is beneficial to the effective management and application of algorithms,so as to help researchers improve re⁃search efficiency and adopt cutting-edge achievements【. Method/process】Firstly,the GAN algorithm abstract is used as corpus to anno⁃tate algorithm terms by combining manual annotation with rule extraction,and the BERT-BiLSTM-CRF model is used to realize auto⁃matic extraction of algorithm terms.Then,the established model is applied to extract algorithm terms from the cited literature metadata of LDA algorithm papers,and the innovative evolution path of LDA algorithm is extracted from the cited content according to rule judg⁃ment and citation relationship【. Result/conclusion】In the algorithm term experiment with GAN paper as an example,the accuracy rate,recall rate and F1 score reach 0.81,0.63 and 0.71 respectively,and we construct the innovation evolution path of the LDA using rela⁃tionship extraction.Our method can effectively promote the construction of the algorithm evolution network and the retrieval task of al⁃gorithms,and also enrich the related research of the innovation diffusion theory【. Innovation/limitation】It expands the application field of named entity recognition technology and provides a good idea for computer algorithm management. The subsequent construction method of innovation evolution path can be optimized.