Titanium alloys are widely used in aerospace and other fields， and optimizing their microstructure is the key to improving material properties. Therefore， exploring the correlation between thermal processing parameters and microstructure evolutionary patterns of titanium alloys has become a research hotspot. Traditional research methods heavily rely on experimental methods， obtaining data through physical sample preparation and characterization. However， the complex combinations， long experimental cycles， and high costs of thermal processing conditions make it difficult to obtain complete microstructural data across all operating conditions. To address the above issues， a cross-condition microstructure data generation method for titanium alloys based on improved Conditional Generative Adversarial Network （CGAN）， namely GL-CGAN-TL， was proposed. The proposed method consists of two modules： first， a microstructure data generation module based on global-local generator fusion， which efficiently generates microstructure data under new operating conditions through the global-local generator； second， a data quality transfer validation module based on adaptive fine-tuning， which verifies the quality of generated data by combining transfer learning technology. Experimental results on the microstructure sample data of TC18 titanium alloy show that the proposed method can maintain the generated data completing the real data effectively， while reducing the Mean Absolute Error （MAE） and Root Mean Square Error （RMSE） by more than 27.6% compared to the existing methods， which is significantly better than similar methods.