Reliability prediction of consumer purchase intention based on an improved Bayesian algorithm

With the development of computer technology and Internet technology, people's production and life has become more and more convenient, and then the accumulation of data is getting bigger and larger. These massive data contain rich knowledge and laws behind it. If these useful information can be mined out, it will be of great help to our future life, especially in commodity trading. In view of some deficiencies of naive Bayesian classification algorithm and Bayesian network algorithm in the reliability prediction of consumer purchase intention, combined with the advantages of distributed processing data of Hadoop platform and the characteristics of consumers' purchase intention themselves, this paper proposes a Bayesian network classification algorithm running on the basis of MapReduce framework. This paper proves that in the simulated shopping survey of 300 people, the price of goods has great intention to consumers 'purchase intention, and the higher the price of goods, the less the fluctuation of consumers' purchase intention.