The　selling　price　of　a　used　car　can　be　predicted　based　on　its　historical　information.　Accurate　and　reasonable　used　car　price　evaluation　will　be　able　to　promote　the　healthy　progress　of　the　used　car　industry.　Current　used　car　price　prediction　models　are　troubled　by　data　quality　and　the　inability　to　provide　estimates　of　uncertain　information,　and　the　prediction　accuracy　cannot　meet　the　needs　of　real-world　scenarios.　In　this　work,　we　propose　a　price　prediction　model　based　on　a　Bayesian　neural　networks　with　latent　factor　(LFBNN)　in　a　cloud　platform,　which　is　capable　of　performing　latent　factor　extraction　operations　on　structured　data　of　used　cars　as　a　way　to　remove　the　effect　of　noise　in　the　dataset　on　the　model　performance.　Moreover,　the　weight　parameters　of　the　Bayesian　neural　networks　(NNs)　are　represented　as　probability　distributions,　which　is　equivalent　to　introducing　uncertainty　and　acting　as　a　regularization　effect　compared　to　a　NN　with　fixed　weights,　thus　making　it　possible　to　alleviate　the　overfitting　problem.　The　LFBNN　model　uses　a　cloud　platform　for　data　transmission　and　storage,　enabling　it　to　run　faster.　Compared　with　the　current　benchmark　models,　the　model　proposed　in　this　work　achieves　excellent　experimental　results　on　two　real　datasets.　The　experimental　results　on　the　$Car_{1}$　dataset　are　1.498　for　mse,　1.224　for　rmse,　and　0.995　for　MAE,　and　1.519　for　mse,　1.232　for　rmse,　and　1.002　for　MAE　on　the　$Car_{2}$　dataset.　IEEE