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.　©　2024,　HARD　Publishing　Company.　All　rights　reserved.