Research on International Law Data Integrity Guarantee Based on Antiterrorism Prediction Algorithm

Abstract

In order to improve the quality of international law data, this paper designs a method to ensure the integrity of international law data based on an antiterrorism prediction algorithm. On the basis of introducing random function and deep learning technology, the prediction model set is split through the trusted seed model, the model selection is completed through iterative optimization, and then new terrorism-related factors are added to obtain the new prediction model set, so as to complete the antiterrorism data prediction. Based on the antiterrorism prediction data obtained by the above process, on the basis of identifying incomplete data, the antiterrorism data integrity guarantee method in international law database is designed through the process of determining data integrity operation parameters, eliminating worthless feature items, processing noise dimension reduction, and adding feature series of data integrity. Experimental results show that the frequency range and data sensitivity of international law data can be optimized by using this method, the audit process of data integrity is less time consuming, and the data integrity after processing can reach 0.967.