数据智能检索, 数据模糊检索,  高维重叠数据, 混合语义相似度, 数据检索, 智能检索

In the process of intelligent data retrieval, the limitation of a single semantic similarity may affect the judgment of data relevance, resulting in a low ranking sensitivity of the retrieval results. To alleviate this problem, an intelligent fuzzy retrieval algorithm is proposed for high-dimensional overlapping data under hybrid semantic similarity extraction. The information granularity space of high-dimensional overlapping data is established. Through density clustering and fuzzy measurement, the regions where the overlapping data is located in the space are identified, thereby achieving the dimension reduction of high-dimensional data without changing the data structure. Three semantic similarity calculation methods, namely the mixed mean function, approximate inearization statistics, and rule method, are used to comprehensively analyze the correlation of dimensionality reduction data. Under the traversal of the nearest neighbor algorithm, the data information with higher semantic similarity is extracted in sequence to generate the data retrieval list. The results of the case study show that the average NDCG(Normalized Discounted Cumulative Gain) index value exhibited by this algorithm is 0. 867. The retrieval results have a high ranking sensitivity and higher retrieval quality, and have a good practical application prospect.

data intelligent retrieval, fuzzy data retrieval, high-dimensional overlapping data, mixed semantic similarity, data retrieval, intelligent retrieval