The automotive industry’s low-carbon transformation is crucial to a nation’s ability to fulfill its “Carbon Peaking and Carbon Neutrality” (CPDN) commitment. However, China’s automotive industry still has a number of issues that have sparked debate. Based on the fact that people use social Q&A platforms to get information, solve problems, and aid in decision-making, negative answers in massive amounts of information typically have a higher degree of information perception and are easier to spread. This work constructed the algorithm of emotion calculation and classification, negative network construction for social Q&A platforms, and carried out empirical research with Zhihu. The 175 questions and 5220 corresponding answers for new energy automobiles were organized as a database to search the development path for the new energy automobile industry. The new energy vehicle industry’s development path primarily entails: resolving the issue of charging difficulty and popularizing charging heaps; attending to the battery safety issue and the head brand of new energy vehicles concentrating notably on quality control. The empirical findings also demonstrate that algorithms developed can more effectively complete the task of sentiment analysis, aid users in making decisions, and contribute to realizing the CPDN goal.