Abstract: Taking Ti-6Al-4V alloy micro turbine blades as the object, the influence analysis of cutting parameters and process optimization research were carried out to address the deformation and surface quality problems that are prone to occur during milling. By using Abaqus software to simulate and analyze the milling process, the influence of cutting parameters on machining deformation and surface quality was systematically studied. On this basis, a surface roughness and deformation prediction model based on 1D-MHA-BiGRU neural network was constructed, and the overall prediction accuracy of the model was good. Further use genetic algorithm to optimize the neural network model and obtain the optimal cutting parameter combination. The results showed that the optimized cutting parameters reduced machining deformation by 32.1%, improved surface roughness by 41.7%, and the error between predicted results and actual machining values remained between 5% and 6%. This study has effectively optimized the micro milling process of titanium alloy turbine blades, which has good guiding significance for improving their machining accuracy and surface quality.