Relying　on　a　data-driven　methodology,　deep　learning　has　emerged　as　a　new　approach　for　dynamic　resource　allocation　in　large-scale　cellular　networks.　This　paper　proposes　a　knowledge-assisted　domain　adversarial　network　to　reduce　the　number　of　poorly　performing　base　stations　(BSs)　by　dynamically　allocating　radio　resources　to　meet　real-time　mobile　traffic　needs.　Firstly,　we　calculate　theoretical　inter-cell　interference　and　BS　capacity　using　Voronoi　tessellation　and　stochastic　geometry,　which　are　then　incorporated　into　a　neural　network　as　key　parameters.　Secondly,　following　the　practical　assessment,　a　performance　classifier　evaluates　BS　performance　based　on　given　traffic-resource　pairs　as　either　poor　or　good.　Most　importantly,　we　use　well-performing　BSs　as　source　domain　data　to　reallocate　the　resources　of　poorly　performing　ones　through　the　domain　adversarial　neural　network.　Our　experimental　results　demonstrate　that　the　proposed　knowledge-assisted　domain　adversarial　resource　allocation　(KDARA)　strategy　effectively　decreases　the　number　of　poorly　performing　BSs　in　the　cellular　network,　and　in　turn,　outperforms　other　benchmark　algorithms　in　terms　of　both　the　ratio　of　poor　BSs　and　radio　resource　consumption.　IEEE