For the purpose of enhancing the precision of 3-D reconstruction based on the structured light method, the regression model in machine learning was used to measure the 3-D topography of objects. The light intensity information cluster samples in different directions of object height points were obtained monocular as the training set of the regression model. After the regression model was trained, the mapping function relationship between the illumination intensity information distribution of the modulation diagram and the height of the object can be directly established to complete the three-dimensional measurement of the object. The numerical information of modulated fringe light was introduced into the regression model in the form of characteristics. 3-D surface of the object was accurately reconstructed, and the purpose of obtaining the height information from end to end was realized. The feasibility of the neural network regression model based on machine learning in 3-D surface reconstruction was verified. The results show that the model can reconstruct the 3-D surface accurately even when the projection features are fuzzy or the noise is large. The average reconstruction error is 1.40×10-4 mm, which is better than the data of the general reconstruction method. This study provides a reference for the high-precision 3-D surface reconstruction of monocular objects under strong interference conditions, effectively simplifies the tedious calculation and measurement process, and improves measurement accuracy.