Research on transaction optimization strategy based on data analysis of second-hand car trading platform

With the development of the Internet, more and more service-oriented industries are transforming from stores to build platforms on the internet, and so is the second-hand car sales industry, which not only saves the cost of opening stores and employees, but also facilitates the majority of car enthusiasts. But changing the transaction model to O2O is even more technical and professional car issues transferred to the owners and buyers. Consider that some sellers or buyers will have inadequate preparation and thus suffer from the transaction. Therefore, the platform needs to give estimated prices based on previous normal transaction data and after confirming the owner's real submission of used car information, so that the owner can adjust between prices, thus ensuring the quality and speed of the transaction. In this paper, we used desensitized data on second-hand car transactions provided by WUBA. After data cleaning, the main model was constructed by neural network, and the model was trained with the processed data. After validating the model, the factors that affected the transaction cycle were found out and optimized the model.