In structured light systems, measurement accuracy tends to decline significantly when evaluating complex textured surfaces, particularly at boundaries between different colors. To address this issue, this paper conducts a detailed analysis to develop an error model that illustrates the relationship between phase error and image characteristics, specifically the blur level, grayscale value, and grayscale gradient. Based on this model, a high-precision approach for measuring complex textured targets is introduced, employing a multiple filtering approach. This approach first applies a sequence of filters to vary the blur level of the captured patterns, allowing calculation of phase differences under different blur conditions. Then, these phase differences are used in the constructed error model to identify the critical parameter causing phase errors. Finally, phase recovery is performed using the calibrated parameter, effectively reducing errors caused by complex textures. Experimental comparisons exhibit that this method reduces the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) by 40.31% and 40.78%, respectively. In multiple experiments, its performance generally surpassed that of existing methods, demonstrating improved accuracy and robustness.