Most　of　networks　in　real　world　obviously　present　dynamic　characteristics　over　time,　and　the　community　structure　of　adjacent　snapshots　has　a　certain　degree　of　instability　and　temporal　smoothing.　Traditional　Temporal　Trade-off　algorithms　consider　that　communities　found　at　time　t　depend　both　on　past　evolutions.　Because　this　kind　of　algorithms　are　based　on　the　hypothesis　of　short-term　smoothness,　they　can　barely　find　abnormal　evolution　and　group　emergence　in　time.　In　this　paper,　a　Dynamic　Community　Detection　method　based　on　an　improved　Evolutionary　Matrix　(DCDEM)　is　proposed,　and　the　improved　evolutionary　matrix　combines　the　community　structure　detected　at　the　previous　time　with　current　network　structure　to　track　the　evolution.　Firstly,　the　evolutionary　matrix　transforms　original　unweighted　network　into　weighted　network　by　incorporating　community　structure　detected　at　the　previous　time　with　current　network　topology.　Secondly,　the　Overlapping　Community　Detection　based　on　Edge　Density　Clustering　with　New　edge　Similarity　(OCDEDC_NS)　algorithm　is　applied　to　the　evolutionary　matrix　in　order　to　get　edge　communities.　Thirdly,　some　small　communities　are　merged　to　optimize　the　community　structure.　Finally,　the　edge　communities　are　restored　to　the　node　overlapping　communities.　Experiments　on　both　synthetic　and　real-world　networks　demonstrate　that　the　proposed　algorithm　can　detect　evolutionary　community　structure　in　dynamic　networks　effectively.