Bayesian Network Structure Learning and Application

Abstract

With the continuous development of artificial intelligence technology and information technology, a large number of background data are constantly generated. How to obtain effective and useful data in a large and complex data group becomes important and meaningful. The traditional Bayesian network can represent the probability distribution of data variables from a large number of data based on graphical models. It has relatively clear and reliable reasoning ability and decision-making mechanism. However, the traditional Bayesian network structure has serious shortcomings in the recognition accuracy of corresponding key data, so the efficiency of the corresponding algorithm is seriously low. Based on this, this study adds an adaptive genetic algorithm with causality to the original Bayesian structure, so as to optimize the strategy of its structure operation, quantitatively describe the order of the corresponding data nodes, creatively arrange the corresponding data nodes in order by using the node priority, and initialize the initial architecture of Bayesian network based on this. Finally, the network is initialized through information exchange and data score correction, so as to get the final learning results. In this study, the convolution neural network algorithm in a database is verified in the experiment. The experimental results show that the accuracy of the experimental results given by the Bayesian network structure proposed in this study is about 10% higher than the traditional accuracy, and its corresponding learning results basically cover the important algorithms, hypotheses, and verification of convolution neural network, from this level; the algorithm proposed in this study has obvious advantages in bibliometrics.