The　diverse　window-opening　behaviors　of　individuals　can　result　in　significant　differences　in　indoor　thermal　environments,　air　quality,　and　energy　utilization.　However,　the　majority　of　existing　studies　focus　on　constructing　an　average　window　operation　model,　thus　overlooking　the　diversity　of　behaviors.　Current　methods　for　addressing　behavioral　diversity　face　challenges　with　integration　into　building　performance　simulation　software　and　are　highly　dependent　on　data　scale.　To　address　these　limitations,　this　study　proposes　a　novel　approach　that　combines　unsupervised　learning　(K-Means)　and　supervised　learning　(Light　Gradient　Boosting　Machine,　LightGBM)　for　modeling　the　diverse　window-opening　behaviors.　Furthermore,　the　SHapley　Additive　exPlanations　(SHAP)　was　employed　to　interpret　the　predictive　model.　This　study　yielded　four　key　findings:　1)　There　were　12　different　window-opening　behavior　patterns.　Interestingly,　65　%　of　the　residents　＇　window-opening　behaviors　were　not　influenced　by　environmental　factors　but　were　instead　a　matter　of　personal　habit.　2)　Using　random　sampling　to　divide　the　dataset　may　pose　a　risk　of　data　leakage.　The　time　series　cross-validation　method　is　more　suitable　for　evaluating　the　performance　of　the　window　state　prediction　model.　3)　Under　the　time　series　sampling　strategy,　the　LightGBM　model　incorporating　behavioral　diversity　improved　the　prediction　accuracy　by　1.3%　-　10.4　%　compared　to　the　standalone　LightGBM　model.　Notably,　when　the　daily　average　window　opening　time　was　used　as　a　clustering　feature　in　the　LightGBM　model　(Cluster(T)-LightGBM),　the　accuracy　reached　87.1　%.　4)　The　SHAP　feature　analysis　highlighted　high-intensity　window-opening　categories,　outdoor　temperature,　and　indoor　CO　2　concentration　as　the　most　pivotal　predictors.