Multi-turn　response　selection　is　an　important　branch　in　the　field　of　natural　language　processing,　which　aims　to　select　the　most　appropriate　response　based　on　multi-turn　dialogue.　Most　state-of-the-art　models　adopt　pre-trained　language　models　(PrLMs)　and　multiple　auxiliary　tasks　to　enhance　their　ability　to　understand　the　semantics　in　multi-turn　dialogue.　However,　some　critical　challenges　still　remain　to　be　addressed.　Optimizing　multiple　auxiliary　tasks　simultaneously　may　significantly　increase　the　training　cost.　Meanwhile,　the　semantic　gap　between　the　optimization　objectives　of　the　main　and　auxiliary　tasks　may　bring　noise　to　pre-trained　language　models.　To　address　these　challenges,　we　propose　an　efficient　BERT-based　neural　network　model　with　local　context　comprehension　(BERT-LCC)　for　multi-turn　response　selection.　First,　we　propose　a　self　-supervised　learning　strategy,　which　introduces　an　auxiliary　task　named　Response　Prediction　in　Random　Sliding　Windows　(RPRSW).　In　a　multi-turn　dialogue,　the　RPRSW　task　takes　utterances　falling　within　a　random　sliding　window　as　input　and　predicts　whether　the　last　utterance　within　the　sliding　window　is　the　appropriate　response　for　the　local　dialogue　context.　This　auxiliary　task　can　enhance　BERT＇s　understanding　of　local　semantic　information.　Second,　we　propose　a　local　information　fusion　(LIF)　mechanism　that　collects　multi-granularity　local　features　at　different　dialogue　stages　and　employs　a　gating　function　to　fuse　global　features　with　local　features.　Third,　we　introduce　a　simple　but　effective　domain　learning　strategy　to　learn　rich　semantic　information　at　different　dialogue　stages　during　pre-training.　Experimental　results　on　two　public　benchmark　datasets　show　that　BERT-LCC　outperforms　other　state-of-the-art　models.