The weighted total least-squares （WTLS） adjustment is a rigorous method for estimating parameters in the errors-in-variables （EIV） model. However， the WTLS are not proper for larger data problem in terms of computational efficiency. Aimed at the structural characteristics of the design matrix in the EIV model，a partially weighted total least-squares （PWTLS） algorithm is proposed based on weighted least-squares （WLS） adjustment by weighting the random column of the design matrix. The PWTLS can obtain an exact solution of the EIV model without applying Lagrange multipliers in a straightforward manner. In addition， the PWTLS reduces the dimensions of the cofactor matrix and does not estimate the random error of the design matrix， as this would greatly improve the computational efficiency. Finally， real and simulated examples are used to demonstrate the accuracy and computational performance of the proposed algorithms . The results show that the PWTLS can obtain the same accuracy as the existing seven improved algorithms， but the computational efficiency is significantly improved .