Objective: We constructed two neural network models to estimate the three-dimensional ground reaction force (GRF) and center of pressure (COP) during two stance phase of walking. This study might provide a solution for gait analysis without force plate. Methods: Gait dataset were collected over the past 10 years and 1384 gait data were selected. Multi-layer perceptron (MLP) and convolutional neural network (CNN), were constructed to estimate the components of GRF and COP based on the marker’s three-dimensional trajectories. In this study, 100 samples were randomly selected as the test set, and the estimation performance was evaluated by the correlation coefficient (r), relative root mean square error (rRMSE). Paired-sample T-tests were used to compare the estimation performance of the two neural network models. Results: The r values of MLP in each components of GRF were 0.954-0.993, and the rRMSEs were 4.36%-9.83%. The r values of CNN in each components of GRF were 0.979-0.994, and the rRMSEs were 3.06%-6.69%. The r values of MLP in each components of COP were 0.888-0.992, and the rRMSEs were 4.78%-16.63%. The r values of CNN in each components of COP were 0.944-0.995，and rRMSEs were 3.06%-10.85%. The RMSEs of CNN in estimating the medio-lateral component of GRF , the medio-lateral and antero-posterior components of COP during right stance phase, as well as the medio-lateral and antero-posterior components of COP during left stance phase were all lower than those of MLP (P < 0.010). The RMSEs of MLP in estimating the anterior-posterior component of GRF during right stance phase, as well as the anterior-posterior component of COP and the vertical direction of GRF during left stance phase were lower than those of CNN (P < 0.008). Conclusion: Both MLP and CNN achieved good estimation accuracy in estimating GRF and COP during walking based on the trajectories of markers of the full-body. The estimation accuracy of MLP in estimating the anterior-posterior components and vertical component of GRF was better than that of CNN, while the estimation accuracy of CNN in estimating the medio-lateral component of GRF, the anterior-posterior and medio-lateral components of COP were better than that of MLP.