  Co-occurrence networks are an important method in the field of natural
language processing and text mining for discovering semantic relationships
within texts. However, the traditional traversal algorithm for constructing
co-occurrence networks has high time complexity and space complexity when
dealing with large-scale text data. In this paper, we propose an optimized
algorithm based on inverted indexing and breadth-first search to improve the
efficiency of co-occurrence network construction and reduce memory consumption.
Firstly, the traditional traversal algorithm is analyzed, and its performance
issues in constructing co-occurrence networks are identified. Then, the
detailed implementation process of the optimized algorithm is presented.
Subsequently, the CSL large-scale Chinese scientific literature dataset is used
for experimental validation, comparing the performance of the traditional
traversal algorithm and the optimized algorithm in terms of running time and
memory usage. Finally, using non-parametric test methods, the optimized
algorithm is proven to have significantly better performance than the
traditional traversal algorithm. The research in this paper provides an
effective method for the rapid construction of co-occurrence networks,
contributing to the further development of the Information Organization fields.
