Updating Algorithm of Complex Computer Database Based on Artificial Neural Network

There is a problem of low security in the use of complex computer database technology. To solve this problem, the security performance of complex computer database system can be continuously improved by improving the professional skills of technicians. Generally, large-scale data are only updated individually by single series, and the security of data is ensured by means of verification. In order to test the feasibility and accurate performance of the database optimization query method based on ANN (Artificial neural network) designed in this paper, the simulation experiment shows that the reliability of the three algorithms is infinitely close to zero with the increase of time. The reliability of the algorithm in this paper can be considered as a fixed value after about 10h, and it will no longer change with time. The algorithm in reference [7] gradually flattens in 15h, while the algorithm in reference [4] only flattens in 19h. Based on the fact that ANN can make full use of the mined results, it avoids complex computer transmission of constrained subtree database update algorithm corresponding to the original global frequent entries, and ensures the safety of large-scale data transmission in a short time and in a wide range, such as online update of track database.