To address the challenges of reduced population diversity and sensitivity to hyperparameters in solving Symbolic Regression （SR） problems by using genetic evolutionary algorithms， a Deep Symbolic Regression Technique （DSRT） method based on Transformer was proposed. This method employed autoregressive capability of Transformer to generate expression symbol sequence. Subsequently， the transformation of the fitness value between the data and the expression symbol sequence was served as a reward value， and the model parameters were updated through deep reinforcement learning， so that the model was able to output expression sequence that fitted the data better， and with the model’s continuous converging， the optimal expression was identified. The effectiveness of the DSRT method was validated on the SR benchmark dataset Nguyen， and it was compared with DSR （Deep Symbolic Regression） and GP （Genetic Programming） algorithms within 200 iterations. Experimental results confirm the validity of DSRT method. Additionally， the influence of various parameters on DSRT method was discussed， and an experiment to predict the formula for surface pressure coefficient of an aircraft airfoil using NACA4421 dataset was performed. The obtained formula was compared with the Kármán-Tsien formula， yielding a mathematical formula with a lower Root Mean Square Error （RMSE）.

Achieving seal segmentation precisely， it is benefit to intelligent application of the Republican archives. Concerning the problems of serious printing invasion and excessive noise， a network for seal segmentation was proposed， namely U-Net for Seal （UNet-S）. Based on the encoder-decoder framework and skip connections of U-Net， this proposed network was improved from three aspects. Firstly， multi-scale residual module was employed to replace the original convolution layer of U-Net. In this way， the problems such as network degradation and gradient explosion were avoided， while multi-scale features were extracted effectively by UNet-S. Next improvement was using Depthwise Separable Convolution （DSConv） to replace the ordinary convolution in the multi-scale residual module， thereby greatly reducing the number of network parameters. Thirdly， Binary Cross Entropy Dice Loss （BCEDiceLoss） was used and weight factors were determined by experimental results to solve the data imbalance problem of archives of the Republic of China. Experimental results show that compared with U-Net， DeepLab v2 and other networks， the Dice Similarity Coefficient （DSC）， mean Intersection over Union （mIoU） and Mean Pixel Accuracy （MPA） of UNet-S have achieved the best results， which have increased by 17.38%， 32.68% and 0.6% at most， and the number of parameters have decreased by 76.64% at most. It can be seen that UNet-S has good segmentation effect in the dataset of Republican archives.