With　the　appearance　of　a　huge　number　of　reusable　electronic　products,　the　precise　value　evaluation　has　become　an　urgent　problem　to　be　solved　in　the　recycling　process.　Traditional　methods　rely　on　manual　intervention　mostly.　In　order　to　make　the　model　more　suitable　for　the　dynamic　updating,　this　paper　proposes　the　reinforcement　learning　based　electronic　products　value　prediction　model,　and　it　integrates　market　information　to　achieve　timely　and　stable　prediction　results.　The　basic　attributes　and　depreciation　attributes　of　the　product　are　modeled　by　two　parallel　neural　networks　separately　to　learn　the　different　effects　for　prediction.　Most　importantly,　the　double　deep　Q　network　is　adopted　to　fuse　market　information　by　reinforcement　learning　strategy,　and　the　training　on　the　old　product　data　can　be　used　to　predict　the　following　appeared　product,　which　alleviates　the　cold　start　problem.　Experiments　on　the　real　mobile　phone　recycling　platform　data　verify　that　the　model　has　achieved　higher　accuracy　and　it　has　a　better　generalization　ability.