Null Value Estimation of Uncertainty Database Based on Artificial Intelligence

Due to the complexity of the objective world, information loss and uncertainty are common. As a tool to express the real world, database uses null values to express the problem of information missing. Aiming at the problem of null value in uncertain database, an artificial intelligence based null value estimation algorithm is proposed. Firstly, the characteristics of uncertain database are analyzed, then the lost information retrieval model is constructed, and the empty value estimation of database is completed by feature selection and data transformation, artificial intelligence clustering, influence degree calculation, empty value step estimation and other methods. Finally, it analyses the time complexity of the algorithm, and improves the problem of poor evaluation effect of traditional algorithms. Supported by experimental data and environment, the results show that the proposed algorithm has higher accuracy than the traditional algorithm. It shows that this algorithm can effectively estimate the null value in the uncertain database, and has high practical application value, and can provide theoretical reference value for related research.