To address the requirements of quality inspection and assembly process control in large-scale high-end equipment manufacturing， an active aiming-based attitude measurement target for laser trackers was developed. A comprehensive mathematical model for attitude measurement was established， accompanied by parameter calibration procedures and nonlinear error compensation methods. The structural composition， operational principles， coordinate system definitions， and data acquisition and preprocessing techniques of the active aiming target were systematically presented. Attitude-solving models were constructed based on constraints derived from laser beam vectors and gravitational vectors. Furthermore， a feedforward neural network was employed to compensate for nonlinear errors inherent in the model-derived attitude data. Calibration strategies for parameter matrices and neural network training were formulated and evaluated. Experimental validation， including parameter calibration and attitude measurement， was performed using the active aiming target. The results indicate that within a 15 m range， the root-mean-square deviation between the proposed target and a Leica T-MAC sensor is 0.030°， satisfying the precision requirements for typical industrial large-scale attitude measurements.