Aspect-based sentiment analysis based on pre-trained models generally uses end-to-end frameworks， has the problems of inconsistency between the upstream and downstream tasks， and is difficult to model the relationships between aspect words and context effectively. To address these problems， an aspect-based sentiment analysis method integrating prompt knowledge was proposed. First， in order to capture the semantic relation between aspect words and context effectively and enhance the model’s perception ability for sentiment analysis tasks， based on the Prompt mechanism， a prompt text was constructed and spliced with the original sentence and aspect words， and the obtained results were used as the input of the pre-trained model Bidirectional Encoder Representations from Transformers （BERT）. Then， a sentimental label vocabulary was built and integrated into the sentimental verbalizer layer， so as to reduce search space of the model， make the pre-trained model obtain rich semantic knowledge in the label vocabulary， and improve the learning ability of the model. Experimental results on Restaurant and Laptop field datasets of SemEval2014 Task4 dataset as well as ChnSentiCorp dataset show that the F1-score of the proposed method reaches 77.42%， 75.20% and 94.89% respectively， which is increased by 0.65 to 10.71， 1.02 to 9.58 and 0.83 to 6.40 percentage points compared with the mainstream aspect-based sentiment analysis methods such as Glove-TextCNN and P-tuning. The above verifies the effectiveness of the proposed method.

With the deepening of population aging， fall detection has become a key issue in the medical and health field. Concerning the low accuracy of fall detection algorithms in complex scenes， an improved fall detection model PDD-FCOS （PVT DRFPN DIoU-Fully Convolutional One-Stage object detection） was proposed. Pyramid Vision Transformer （PVT） was introduced into the backbone network of baseline FCOS algorithm to extract richer semantic information without increasing the amount of computation. In the feature information fusion stage， Double Refinement Feature Pyramid Networks （DRFPN） were inserted to learn the positions and other information of sampling points between feature maps more accurately， and more accurate semantic relationship between feature channels was captured by context information to improve the detection performance. In the training stage， the bounding box regression was carried out by the Distance Intersection Over Union （DIoU） loss. By optimizing the distance between the prediction box and the center point of the object box， the regression box was made to converge faster and more accurately， which improved the accuracy of the fall detection algorithm effectively. Experimental results show that on the open-source dataset Fall detection Database， the mean Average Precision （mAP） of the proposed model reaches 82.2%， which is improved by 6.4 percentage points compared with that of the baseline FCOS algorithm， and the proposed algorithm has accuracy improvement and better generalization ability compared with other state-of-the-art fall detection algorithms.