In　recent　years,　the　use　of　machine　learning　methods　to　deal　with　the　problem　of　user　interest　prediction　has　become　a　hot　research　direction　in　the　field　of　electronic　commerce.　In　the　present　stage,　a　naive　Bayesian　algorithm　has　the　advantages　of　simple　implementation　and　high　classification　efficiency.　However,　this　method　is　too　dependent　on　the　distribution　of　samples　in　the　sample　space,　and　has　the　potential　of　instability.　To　this　end,　the　decision　tree　method　is　introduced　to　deal　with　the　problem　of　interest　classification,　and　the　innovative　use　of　Localstorage　technology　in　HTML5　to　obtain　the　required　the　experimental　data.　Classification　method　uses　the　information　entropy　of　the　training　data　set　to　build　the　classification　model,　through　the　simple　search　of　the　classification　model　to　complete　the　classification　of　unknown　data　items.　Both　theoretical　analysis　and　experimental　results　show　that　the　decision　tree　is　used　to　deal　with　the　problem　of　prediction　of　users＇　interests　has　obvious　advantages　in　the　efficiency　and　stability.　©　2016　IEEE.