Cognitive Computing Model Based on Machine Learning Algorithm in Artificial Intelligence Environment

Abstract

In order to effectively solve problems and complete data processing quickly, a data processing model based on computational knowledge training technology is prepared. First, prepare the cognitive model and then learn the data as techniques (distribution, decision-making, etc.) that can be used in the model and the process of scheduling work in the environment. The main points and progress of the research are as follows. Many people see the purpose of the problem that the data and data streams are difficult to identify and influence. The flow of knowledge data in relation to the use of the Internet is based on content-based counting patterns. It is to learn to correctly analyze the information points. In addition, in view of the problem of unsatisfactory effect caused by the randomness of the optional splitting behavior in the VFDT (Fast Decision Tree) algorithm, the decision tree algorithm for data flow is planned for the distribution of data flow. The accuracy of the algorithm can be close to 96%. The performance of the experiments was evaluated.