LDA-Neural Network Classification Of Net Vehicle User Satisfaction

The "Internet + travel" network car has emerged in the information age,and its core competitiveness has shifted from pure technical competition to service quality user satisfaction competition.The satisfaction of the network car users includes safety,economy,convenience,platform friendliness and comfort.In order to improve their competitiveness in the industry,the network car platform urgently needs to establish a real-time tracking user satisfaction.The classification analysis system provides a reference basis for the decision-making of the network-based vehicle platform,forms a new engine for the healthy development of the network-related car industry,promotes coordinated and stable development of the society,and improves the happiness index of residents.The paper makes use of the unique advantages of the network car platform to compile a survey questionnaire for five aspects of satisfaction for the user.The questionnaire is designed as an anonymous answer.The user answers correspond to the owner and the corresponding car condition.The alternative language of the problem is described and refined.Accurate,users generally feedback information,especially users with low satisfaction show high enthusiasm;after collecting sample data,singular sample data is eliminated,missing data is filled with median,and reliability is analyzed.Degree analysis judges the accuracy and stability of the data;after confirming the reliability and stability of the data,the Fischer discriminant criterion is used to screen the main indicators that affect the satisfaction,and the difficulty of the model is reduced.Finally,the satisfaction will be The main influence indicator is used as the input of the neural network,and the weight of the neural network is trained by the level of user satisfaction as the output.The well-trained neural network is a classifier for user satisfaction.The network car platform can use the classification results of the neural network classifier to introduce relevant marketing measures to form data-based decisions for the network car platform and enhance competition in the same industry.Strength,to maximize the benefits of the network car platform.