The　multi-turn　response　selection　is　an　important　component　in　retrieval-based　human–computer　dialogue　systems.　Most　recent　models　adopt　the　utilization　of　pre-trained　language　models　to　acquire　fine-grained　semantic　information　within　diverse　dialogue　contexts,　thereby　enhancing　the　precision　of　response　selection.　However,　effectively　leveraging　the　language　style　information　of　speakers　along　with　the　topic　information　in　the　dialogue　context　to　enhance　the　semantic　understanding　capability　of　pre-trained　language　models　still　poses　a　significant　challenge　that　requires　resolution.　To　address　this　challenge,　we　propose　a　BERT-based　Language　Style　and　Topic　Aware　(BERT-LSTA)　model　for　multi-turn　response　selection.　BERT-LSTA　augments　BERT　with　two　distinctive　modules:　the　Language　Style　Aware　(LSA)　module　and　the　Question-oriented　Topic　Window　Selection　(QTWS)　module.　The　LSA　module　introduces　a　contrastive　learning　method　to　learn　the　latent　language　style　information　from　distinct　speakers　in　the　dialogue.　The　QTWS　module　proposes　a　topic　window　segmentation　algorithm　to　segment　the　dialogue　context　into　topic　windows,　which　facilitates　the　capacity　of　BERT-LSTA　to　refine　and　incorporate　relevant　topic　information　for　response　selection.　Experimental　results　on　two　public　benchmark　datasets　demonstrate　that　BERT-LSTA　outperforms　all　state-of-the-art　baseline　models　across　various　metrics.　Furthermore,　ablation　studies　reveal　that　the　LSA　module　significantly　improves　performance　by　capturing　speaker-specific　language　styles,　while　the　QTWS　module　enhances　topic　relevance　by　filtering　irrelevant　contextual　information.　©　2025