A High Performance Text Vector Similarity Search Method Based on Overlapping Degree Similarity search of massive highdimensional data is an important issue in the field of text data computation and search, which seriously affects the efficiency and convenience of text search and use. This paper proposes a highperformance text vector similarity search method, and proposes overlapping degree to describe the statistical characteristics of query vectors recurring in inverted index. Based on overlapping degree, candidate data sets are generated and sorted, distance calculation is carried out in candidate data set sequence, unnecessary calculation is reduced, and computation scale is reduced. Similar vectors of query vectors can be quickly obtained with very short index construction time and small memory overhead. The experimental results show that compared with the representative four methods, the index construction time of our method is shortened to 22223 times, the index size memory overhead is reduced by 218 times, and the query speed advantage can be obtained with high recall rate, which greatly improves the performance and efficiency of text data similarity search.

Cartesian Tree Matching and Indexing We introduce a new metric of match, called Cartesian tree matching, which means that two strings match if they have the same Cartesian trees. Based on Cartesian tree matching, we define single pattern matching for a text of length n and a pattern of length m, and multiple pattern matching for a text of length n and k patterns of total length m. We present an O(nm) time algorithm for single pattern matching, and an O((nm) log k) deterministic time or O(nm) randomized time algorithm for multiple pattern matching. We also define an index data structure called Cartesian suffix tree, and present an O(n) randomized time algorithm to build the Cartesian suffix tree. Our efficient algorithms for Cartesian tree matching use a representation of the Cartesian tree, called the parentdistance representation.