Research On The Identification Of New Energy Vehicle Product Quality Improvement Based On Online Reviews

With the high-quality development and transformation of China’s automobile industry,the number of new energy vehicles has increased significantly,and the product quality problems have entered a concentrated outbreak stage,which seriously restricts the development of new energy vehicle industry.Product quality improvement has a positive impact on the sustainability of the new energy vehicle industry.The younger and personalized new energy vehicle users put forward higher requirements on the timeliness and accuracy of product quality improvement.However,due to the traditional quality feedback methods(questionnaire,telephone interview and research,etc.),it is unable to timely and accurately identify users’quality improvement needs.As a reliable source of business intelligence,online reviews provide a new ecological soil for the identification of new energy vehicle quality improvement.Based on the quality feedback perspective of online reviews,this paper uses deep learning,dependency parsing,Kano model and quality function deployment QFD methods to build a new energy vehicle product quality improvement identification framework,and carries out an empirical study on the T plug-in hybrid version of new energy vehicles,and specifically completes the following work.(1)Firstly,the micro theory of new energy vehicle quality improvement is improved.This paper analyzes the similarities and differences of quality improvement between automobile and other products,new energy vehicle and traditional vehicle,’ and establishes a theoretical basis for the quality improvement of new energy vehicle products.(2)Secondly,a new energy vehicle user quality improvement identification framework based on online reviews is constructed.35 kinds of hot new energy vehicle reviews are captured and preprocessed by word segmentation,stop.word removal and part of speech tagging.In order to identify quality problems accurately,the short-term and long-term memory model LSTM is selected to classify negative comments.The negative comments are analyzed by dependency syntax,and six aspect level opinion extraction rules are obtained.Combined with the dictionary,the aspect level opinions are quantified,and the Kano model is used for demand classification.(3)Finally,the QFD method based on online reviews is constructed,and an empirical study is carried out on the T plug-in hybrid version of new energy vehicles.15 users’ quality improvement requirements are identified,and the quality tool affinity graph kJ is used to decompose the quality improvement requirements hierarchically.Experts are invited to analyze and score 17 corresponding engineering quality characteristics and relationship matrices,and then the market competition matrix is obtained by using the user score.All the matrices are input into the house of quality,and the conclusions and suggestions related to product quality improvement are obtained,which further proves the feasibility and effectiveness of the method.This paper proposes a new energy vehicle quality problem identification framework based on online review,which provides ideas,framework and tools for solving quality improvement problems driven by review data,and supplements and deepens the research on quality management driven by big data.It not only helps enterprises to identify new energy vehicle quality risks,but also helps relevant quality and design personnel to better understand the quality improvement needs of users,and provides decision-making reference for product quality improvement.