In　ultra-precision　machining　(UPM),　linear　axis　tracking　affects　contour　accuracy　and　final　machining　quality.　Traditional　error　modeling　is　complicated　by　the　identification　of　numerous　unknown　parameters　linked　to　nonlinear　characteristics　in　the　linear　feed　axes.　To　fill　this　gap,　this　study　proposed　a　digital-twin-driven　framework　integrating　the　developed　G-code　interpreter　and　the　deep　learning　model　to　achieve　real-time　tracking　error　compensation　for　UPM.　To　enhance　the　prediction　accuracy　of　the　tracking　error　of　each　axis　of　UPM　machines,　Bayesian　hyperparameter　optimization　and　feature　importance　analysis　were　conducted　in　the　proposed　TCN-BiLSTM　model　using　high-quality　training　datasets　from　well-designed　experiments.　Ultimately,　validation　of　the　proposed　system　on　a　three-axis　ultra-precision　milling　machine　demonstrated　its　excellent　performance.　The　experimental　results　showed　that　the　optimized　TCN-BiLSTM　model　exhibited　an　excellent　capacity　to　predict　the　tracking　error　of　the　X-axis　and　Y-axis　with　minimal　mean　absolute　error　values　of　0.000009　and　0.000023,　respectively.　Implementing　the　customized　application　reduced　X-axis　and　Y-axis　tracking　errors　by　approximately　45–75%　and　40–70%,　respectively.　This　study　first　validates　the　feasibility　of　deep　learning　to　improve　accuracy　in　the　UPM　field,　which　will　provide　significant　insight　into　speeding　up　the　digitalization　and　intellectualization　of　the　UPM　scenario.　©　2024　Elsevier　Ltd