With　the　popularity　of　the　Internet　and　mobile　terminals,　the　development　of　e-commerce　has　become　hotter.　Therefore,　e-commerce　research　starts　to　focus　on　the　statistics　and　prediction　of　the　cargo　volume　of　logistics.　This　study　briefly　introduced　the　back-propagation　(BP)　neural　network　model　and　principal　component　analysis　(PCA)　method　and　combined　them　to　obtain　an　improved　PCA-BP　neural　network　model.　Then　the　traditional　BP　neural　network　model　and　the　improved　PCA-BP　neural　network　model　were　used　to　perform　the　empirical　analysis　of　the　cold　chain　logistics　demand　of　fruits　and　vegetables　in　city　A　from　2010　to　2018.　The　results　showed　that　the　main　factors　that　affected　the　local　cold　chain　logistics　demand　were　the　growth　rate　of　GDP,　the　added　value　of　primary　industry,　the　planting　area　of　fruits　and　vegetables,　and　the　consumption　price　index　of　fruits　and　vegetables;　both　kinds　of　neural　networks　model　could　effectively　predict　the　cold　chain　logistics　demand,　but　the　predicted　value　of　the　PCA-BP　neural　network　model　was　more　fitted　with　the　actual　value.　The　prediction　error　of　the　BP　neural　network　model　was　larger,　and　the　fluctuation　was　obvious　within　the　prediction　interval.　Moreover,　the　time　required　for　the　prediction　by　the　PCA-BP　neural　network　model　was　less　than　that　by　the　BP　neural　network　model.　In　summary,　the　improved　PCA-BP　neural　network　model　is　faster　and　more　accurate　than　the　traditional　BP　model　in　predicting　the　cold　chain　logistics　demand.　©　Y.　Chen　et　al.,　published　by　EDP　Sciences,　2020.