The　rapid　expansion　of　the　Internet　and　the　utilization　of　big　data　have　significantly　contributed　to　a　transformative　shift　in　the　tourism　industry.　As　online　travel　reviews　become　more　abundant,　they　provide　insights　into　sentiments　and　attitudes　related　to　travel　experiences.　This　paper　mainly　concentrates　on　sentiment　analysis　of　travel　reviews　utilizing　deep　learning　methods　and　Transformer　models.　In　particular,　we　explore　the　benefits　of　deep　learning,　specifically　the　Bi-LSTM,　BERT,　and　ERNIE　models.　Rigorous　comparative　experiments　on　a　database　comprising　6,000　travel　reviews　from　Henan　Province,　China　are　conducted.　Experimental　results　demonstrate　the　advantage　of　the　ERNIE　model,　which　incorporates　knowledge　integration　and　diverse　training　tasks.　The　ERNIE　model　achieves　a　prominent　enhancement　in　accuracy,　recall　and　F1　score　compared　to　the　previous　models.　The　findings　underscore　the　efficacy　of　pre-trained　language　models　in　sentiment　analysis　tasks　and　their　capacity　to　comprehend　context　and　semantic　nuances,　leading　to　enhanced　performance　in　sentiment　classification.　©　2024　SPIE.