[Objective]　To　reduce　semantic　deviation　and　loss　caused　by　language　differences　and　text　feature　selection　in　the　text　classification　process　while　preserving　more　textual　information.　[Methods]　Firstly,　we　used　a　pre-trained　SBERT　model　for　sentence　representation.　Secondly,　we　calculated　the　sentence　similarity　between　texts　with　a　Sentence　Vectors　Rotator’s　Similarity　method.　We　also　applied　sentence　weighting　within　texts　to　form　vectors.　Finally,　we　combined　machine　learning　and　neural　network　classification　methods　to　achieve　cross-lingual　text　classification.　[Results]　We　conducted　experiments　on　multiple　cross-lingual　text　datasets　in　Chinese,　English,　Russian,　French,　and　Spanish,　and　the　multilingual　public　dataset　Reuters　demonstrated　that　the　proposed　method　significantly　improved　accuracy　compared　to　existing　methods.　Additionally,　recall,　precision,　and　F1　scores　also　showed　enhancements.　[Limitations]　The　study　does　not　consider　the　impact　of　sentence　position　within　the　text　on　its　weight.　[Conclusions]　The　proposed　model　could　reduce　semantic　deviation　and　loss,　thus　improving　the　performance　of　cross-lingual　text　classification.　©　2025　Chinese　Academy　of　Sciences.　All　rights　reserved.