Fast retrieval method of biomedical literature based on feature mining

In order to solve the problems of large errors, low accuracy of feature mining and time-consuming traditional literature retrieval methods, this paper designs a fast retrieval method for biomedical literature based on feature mining. First, we simulate the document collection space, and collect documents according to the data centroid and probability density function. Secondly, the location of similar data is marked by mutual information method, and the hidden information of literature data is extracted after reducing the imbalance of dataset. Then, the Pearson correlation coefficient of the literature data is calculated and the key features of the literature are mined. Finally, we calculate the expected loss risk of literature data, design a fast retrieval algorithm for biomedical literature, and realise fast retrieval. The test results show that this method can reduce the retrieval error, improve the accuracy of document feature mining, and the retrieval time is shorter.