Fan Zhang
ABSTRACT
The rapid development of new energy vehicles has caused dissatisfaction and diversified needs of users in different usage scenarios. Therefore, it is particularly important to study user satisfaction and future demand for new energy vehicles in different scenarios and it is also important to study ways to optimize and improve them. The paper proposes to use the QFD method for research. Firstly, search for user car review sentences and determine the evaluation indicators; secondly, build car scenes and classify the review sentences; thirdly, use the naive Bayes sentiment algorithm to satisfy the car review sentences, at the same time, the mention rate is used to calculate the importance of the index; finally, after subtracting the model, get the index with low satisfaction score and high importance, use the QFD model to calculate and analyze the index, and propose car improvement suggestions. The research results reflect the current satisfaction situation of the new energy vehicle industry to a certain extent, accurately grasp the pain points and needs of users in different car scenarios, and have reference significance for car companies to improve the satisfaction of new energy vehicles and develop new products.
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