Action Quality Assessment （AQA） has attracted many researchers as a challenging visual task. Current research methods mainly focus on improving the feature extraction capability of backbone networks， ignoring the impact of motion trajectories. However， the consistency of the movements is also an important factor for evaluating execution of the movements in the real world. Firstly， in order to realize the interactive learning between different information， an AQA model with trajectory-guided perceptual learning was proposed by introducing trajectory information， which utilized trajectory descriptors to guide the model to learn information of the consistency of movements perceptually. Secondly， in order to solve the lack of trajectory labels in the current datasets， an unsupervised optical flow trajectory extraction method based on Farneback optical flow method was designed to obtain movement trajectory information， and the acquired optical flow trajectory features were used as cue words to guide the model to learn the video features perceptually. Finally， learnable spline curves of KAN （Kolmogorov-Arnold Network） were used to fit the data distribution of the mixed features， so as to establish a more accurate mapping relationship. The proposed model was evaluated experimentally on the MTL-AQA， AQA-7， FineDiving， and JIGSAWS datasets using Spearman rank Correlation （Sp.Corr） as the evaluation metric. The results show that the proposed model has the Sp.Corr of 0.910 1， 0.912 0， 0.882 0， and 0.990 0， respectively， which is 0.4%， 12.6%， 6.2%， and 57.1% higher than that of USDL （Uncertainty-aware Score Distribution Learning） model， respectively.

In order to solve the problem of most current research work only considering the factors affecting pedestrian interaction， based on State-Refinement Long Short-Term Memory （SR-LSTM） and attention mechanism， a Social-Interaction Generative Adversarial Network （SIGAN） for pedestrian trajectory prediction was proposed， namely SRA-SIGAN， where GAN was utilized to learn movement patterns of target pedestrians. Firstly， SR-LSTM was used as a location encoder to extract the information of motion intention. Secondly， the influence of pedestrians in the same scene was reasonably assigned by setting the velocity attention mechanism， thereby handling the pedestrian interaction better. Finally， the predicted future trajectory was generated by the decoder. Experimental results on several public datasets show that the performance of SRA-SIGAN model is good on the whole. Specifically on the Zara1 dataset， compared with SR-LSTM model，the Average Displacement Error （ADE）and Final Displacement Error （FDE）of SRA-SIGAN were reduced by 20.0% and 10.5%，respectively；compared with the SIGAN model，the ADE and FDE of SRA-SIGAN were decreased by 31.7% and 24.4%，respectively.