Thermal　error　is　a　critical　factor　influencing　the　machining　accuracy　of　CNC　machine　tools,　so　it　is　essential　to　comprehensively　model　and　compensate　for　thermal　errors　in　CNC　machine　tools.　This　paper　proposes　a　deep　attentional　residual　network　thermal　error　prediction　model　driven　by　thermal　image　inputs.　In　contrast　to　traditional　models　that　solely　rely　on　temperature　data,　the　proposed　model　utilizes　thermal　image　data　as　a　key　input　parameter　and　incorporates　temperature　data　from　sensitive　points　to　fully　represent　the　machine＇s　temperature　distribution.　Furthermore,　the　attention　mechanism　is　used　to　optimize　the　hyperparameters　and　network　structure　of　the　residual　network　model.　Transfer　learning　is　employed　to　improve　training　efficiency,　reduce　data　requirements,　and　enhance　the　model＇s　transferability.　The　optimized　model　achieves　a　prediction　accuracy　of　99.5%　and　converges　more　quickly.　Finally,　thermal　error　compensation　experiments　are　conducted　on　the　platform　of　the　Siemens　840D　system　with　an　average　effect　of　more　than　70%.　The　proposed　thermal　error　compensation　method　is　effective　and　provides　a　foundation　for　precision　machining.