As　e-commerce　developing　rapidly,　it　is　becoming　a　research　focus　about　how　to　capture　or　find　customer＇s　behavior　patterns　and　realize　commerce　intelligence　by　use　of　Web　mining　technology.　Recommendation　system　in　electronic　commerce　is　one　of　the　successful　applications　that　are　based　on　such　mechanism.　In　this　paper,　we　present　a　new　framework　in　recommendation　system　by　finding　customer　model　from　business　data.　This　framework　formalizes　the　recommending　process　as　knowledge　representation　of　the　customer　shopping　information　and　uncertainty　knowledge　inference　process.　In　our　approach,　we　firstly　build　a　customer　model　based　on　Bayesian　network　by　learning　from　customer　shopping　history　data,　then　we　present　a　recommendation　algorithm　based　on　probability　inference　in　combination　with　the　last　shopping　action　of　the　customer,　which　can　effectively　and　in　real　time　generate　a　recommendation　set　of　commodity.