Attention Deficit Hyperactivity Disorder （ADHD） is a neurodevelopmental disorder common in childhood， characterized by inattention， hyperactivity， and impulsivity， often exhibiting specific motion patterns. Traditional action recognition algorithms have problems such as low recognition accuracy and slow response when handling these specific actions. To address these issues， an action recognition algorithm for ADHD patients using skeleton and 3D heatmap was proposed， and spatial relationships between joints were represented using Gaussian distribution precisely， which preserved spatio-temporal information effectively. To overcome the limitations of single-modal data， a multimodal integration method based on skeleton and 3D heatmap was introduced. At the same time， the output features of Short 3D-CNN （3D Convolutional Neural Network） and Adaptive Graph Convolutional Network （AGCN） were fused to fully exploit the advantages of both modalities， thereby improving action recognition performance. Experimental results on the ADHD patient dataset collected by Mental Health Center of West China Hospital， Sichuan University， show that the proposed algorithm achieves the Top-1 recognition accuracy of 0.860 4 and the Top-5 recognition accuracy of 0.987 3 for eight different types of actions. Additionally， an automatic ADHD classification algorithm based on action types was proposed， which classified ADHD into head and facial action type， trunk action type， and limb action type， achieving the recognition accuracy of 75% and the response time of 5 seconds. Compared with two-stream AGCN （2s-AGCN） and PoseConv3D， the proposed algorithm demonstrates higher recognition accuracy in complex action scenarios， providing a new technical approach for personalized ADHD intervention.

Item-based collaborative filtering learns user preferences from the user's historical interaction items and recommends similar new items based on the user's preferences. The existing collaborative filtering methods assume that a set of historical items that user has interacted with have the same impact on user, and all historical interaction items are considered to have the same contribution to the prediction of target item, which limits the accuracy of these recommendation methods. In order to solve the problems, a new collaborative filtering recommendation algorithm based on dual most relevant attention network was proposed, which contained two attention network layers. Firstly, the item-level attention network was used to assign different weights to different historical items in order to capture the most relevant items in the user historical interaction items. Then, the item-interaction-level attention network was used to perceive the correlation degrees of the interactions between the different historical items and the target item. Finally, the fine-grained preferences of users on the historical interaction items and the target item were simultaneously captured through the two attention network layers, so as to make the better recommendations for the next step. The experiments were conducted on two real datasets of MovieLens and Pinterest. Experimental results show that, the proposed algorithm improves the recommendation hit rate by 2.3 percentage points and 1.5 percentage points respectively compared with the benchmark model Deep Item-based Collaborative Filtering (DeepICF) algorithm, which verifies the effectiveness of the proposed algorithm on making personalized recommendations for users.