Complex　network　is　now　widely　used　in　a　series　of　disciplines　such　as　biology,　physics,　mathematics,　sociology　and　so　on.　In　this　paper,　we　construct　the　stock　price　trend　network　based　on　the　knowledge　of　complex　network,　and　then　propose　a　method　based　on　information　entropy　to　divide　the　stock　network　into　some　communities,　that　is,　a　gathering　study　of　stock　price　trend.　We　construct　time　series　networks　for　each　stock　in　Chinese　A-share　market　based　on　time　series　network　model,　and　then　use　these　networks　to　divide　the　stock　market　into　communities.　We　find　that　the　average　trend　of　stocks　in　the　same　community　is　the　same　as　the　trend　of　market　value　weighting,　but　the　average　trend　of　stocks　in　different　communities　is　quite　different　and　the　sequence　correlation　is　low.　This　conclusion　shows　that　stocks　in　the　same　community　share　the　same　price　trend,　while　the　stock　trend　in　different　communities　varies.　This　paper　is　a　successful　application　of　complex　network　and　information　entropy　in　stock　trend　analysis,　which　mainly　includes　two　contributions.　First,　the　success　of　the　visibility　graph　algorithm　provides　a　new　perspective　for　enriching　stock　price　trend　modeling.　Second,　our　conclusion　proves　that　the　clustering　based　on　information　entropy　theory　is　effective,　which　provides　a　new　method　for　further　research　on　stock　price　trend,　portfolio　construction　and　stock　return　prediction.