With the rapid development of computer graphics, industrial design, natural science and other fields, the demand for high-quality scientific computing methods is increased. These scientific computing methods are inseparable from high-quality grid generation algorithms. For the commonly used orthogonal grid generation algorithms, whether they can reduce the computational amount and whether the manual intervention can be reduced are still the main challenges faced by them. Aiming at these challenges, for the single-connected target region, an automatic generation algorithm of orthogonal grid was proposed based on Long Short-Term Memory network (LSTM), one of the recurrent neural networks and Schwarz-Christoffel conformal mapping (SC mapping). Firstly, the basic conditions of the Gridgen-c tool based on SC mapping were used to transform the grid generation problem into an integer programming problem with linear constraints. Next, a classifier, which is capable of calculating the probability of the corner type of each vertex of the target polygonal region, was obtained by using the pre-processed GADM dataset and LSTM training. This classifier was able to greatly reduce the time complexity of integer programming problem, making the problem be solved quickly and automatically. Finally, the simple graphics areas, animated graphics areas and geographical boundary areas were taken as examples to conduct a grid generation experiment. Results show that for simple graphic areas, the proposed algorithm can reach the optimal solution on all examples. For animated graphic areas and geographical boundary areas with complex boundaries, the example grid results show that the proposed algorithm can make the calculation amount in these target areas reduced by 88.42% and 91.16% respectively, and can automatically generate better orthogonal grid.