Customer Purchase Preference Model Construction And Customer Resource Analysis System Based On Data Intelligence

In recent years,the development of China's automobile industry has ushered in new challenges and opportunities.With the improvement of living standards,more and more families began to purchase more than one car.The huge demand has also promoted the vigorous development of domestic automobile manufacturing plants.At the same time,automakers of various brands in the world have also entered the Chinese market.However,the difference between the domestic companies and foreign enterprises is getting smaller and smaller,which mean that the company with the advantage of low cost gradually loses its effectiveness.Companies are now focusing on customer preferences and demands,and how to improve the design of automotive products is also important.This paper takes the AA company as a prototype to analyze its vehicle sales and marketing model.At present,the automotive industry chain collaboration platform has perfected the business processes of automobile manufacturers from production to storage,and from car dealers from the next sales order plan to the end of sales.However,due to the lack of sufficient data analysis support,automobile manufacturers and distributors often cannot estimate the real demands of customers.Companies can not accurately mine potential customers from massive customer resources.Therefore,in order to solve this kind of problem,this paper proposes to build a human-vehicle model system based on industrial chain collaboration.Then this paper analyzed the existing demand for vehicle sales and marketing models.Then a new hybrid optimization algorithm is proposed in this paper.This algorithm combines the advantages of genetic algorithms in solving discrete problems and the advantages of swim optimization algorithms in solving continuous problems.The proposed algorithm optimizes the support vector machine by finding the optimal feature subset and the optimal SVM parameter configuration(penalty parameters and kernel function parameters).The main innovation of this algorithm is to propose three parallel operation layers.Two of them are the genetic algorithm operation layer and the swim algorithm operation layer.The third layer is the coordination layer,which is mainly responsible for receiving the individual information of the other two layers and combining them into new individual information for evaluation.Then the evaluation results are returned to the other two layers.Therefore,when the algorithm optimizes SVM,no additional mapping function is needed to convert discrete variables into continuous variables or continuous variables into discrete variables.Finally,this algorithm was successfully applied to the automobile industry chain collaboration vehicle model system.In addition,this paper uses the C# language and adopts a three-tier architecture based on the B/S model.According to the system requirements,this paper has implemented the basic data management module,system management module,human-vehicle model algorithm configuration module.And this paper also implemented other basic functions,and multi-dimensional charts and visual interfaces.