Aspect　level　sentiment　analysis　employs　information　of　terms　to　extract　features　from　a　sentence,　and　it　cannot　utilize　information　of　both　aspects　and　terms　simultaneously.　Therefore,　the　model　performance　is　low.　Aiming　at　this　problem,　an　aspect　level　sentiment　analysis　based　on　recurrent　neural　network　with　auxiliary　memory　is　proposed.　Deep　bidirectional　long　short　term　memory(DBLSTM)　and　positional　information　of　words　are　exploited　to　build　position-weighted　memory.　The　attention　mechanism　is　combined　with　aspect　terms　to　build　aspect　memory,　and　with　position-weighted　memory　and　aspect　memory　to　input　a　multi-layer　gated　recurrent　unit.　Then,　sentimental　features　of　the　aspect　are　obtained.　Finally,　sentimental　polarity　is　identified　by　the　normalized　function.　Experimental　results　show　that　the　proposed　method　achieves　better　results　on　three　public　datasets　with　high　effectiveness.