The work aims to propose a method to measure target pose regarding the relative pose relationship between the camera and target to be determined to control robot to complete wielding, transporting, tracking, etc. in the application field of robot vision. Target characteristics were acquired with single camera and the coordinate transformation parameters were expressed as dual quaternion. Meanwhile, rotation matrix and translation vector were calculated. The error equation between measured values and model values of position vector and direction vector was built. Hopfield neural network and Lagrange multiplier method were used to solve the optimal solution of target pose. Through Matlab software platform, SVD, DQ and the proposed algorithm were selected for comparison. The simulation test results showed that the error of pose parameters calculated by the pose measurement algorithm based on Hopfield neutral network and dual quaterion was the minimum. With the increase in the number of measurement points, the proposed algorithm was of higher accuracy. The simultaneous solution to rotation and translation components of pose transformation matrix for dual quaternion can eliminate calculation errors. Hopfield neural network and Lagrange multiplier method can calculate and converge to the optimal solution of target pose quickly and accurately.