To address the problem of strong feature overfitting and weak feature underfitting problem when learning user representations using Embedding layer & Multi-Layer Perceptron （Embedding&MLP） paradigm for recommendation systems and the problem of learning user interests using Gated Recurrent Unit （GRU） without considering that the influence of current behaviors on users’ final interests diminishes over time， a Recruitment Recommendation Model based on Field Fusion and Time Weight （RecRec） was proposed. In RecRec， firstly， a new domain fusion layer was adopted to replace the traditional tandem layer， and the domain fusion layer showed a significantly superior performance on multi-domain features. Then， time weight was incorporated into GRU in the interest evolution layer， and a TimeStamp Gated Recurrent Unit （TSGRU） was proposed， by which made the user interests were learned more accurately. Ultimately， personalised recommendations were achieved by predicting the dial-up rate of users. Experimental results show that the Area Under Curve （AUC） of RecRec improves by 0.03 to 0.36 percentage points compared to YouTube Deep Neural Network （DNN）， Wide&Deep， Auxiliary LSTM-Attention Matrix Factorization （ALAMF） and Long-term & Short-term Sequential Self-Attention Network （LSSSAN）， indicating that RecRec can effectively learn user representations and user interests.

The accuracy of Inverse Distance Weighting （IDW） will be affected by the selection of reference points and parameters. Aiming at the problem of ignoring local characteristics in multi-Parameter co-optimization Inverse Distance Weighting algorithm （PIDW）， an improved algorithm based on particle swarm local optimized IDW was proposed， namely Particle swarm Local optimization Inverse Distance Weight （PLIDW）. Firstly， the parameters of each sample point in the study area were optimized respectively， and the cross-validation method was used for evaluation， and the optimal set of parameters for each sample point was recorded. At the same time， in order to improve the query efficiency， a K-Dimensional Tree （KD-Tree） was used to save the spatial positions and optimal parameters. Finally， according to the spatial proximity， the nearest set of parameters was selected from KD-Tree to optimize IDW. Experimental results based on simulated data and real temperature dataset show that compared with PIDW， PLIDW has the accuracy on the real dataset improved by more than 4.18%. This shows that the low accuracy in some scenarios caused by ignoring local features in PIDW is improved by the proposed algorithm， and the adaptability is increased at the same time.

The clinicians diagnose Attention Deficit/Hyperactivity Disorder （ADHD） mainly based on on their subjective assessment， which lacks objective criteria to assist. To solve this problem， an automatic detection algorithm for ADHD based on speech pause and flatness was proposed. Firstly， the Frequency band Difference Energy Entropy Product （FDEEP） parameter was used to automatically locate the segment with voice from the speech and extract the speech pause features. Then， Transform Average Amplitude Squared Difference （TAASD） parameter was presented to calculate the voice multi-frequency and extract the flatness features. Finally， fusion features and the Support Vector Machine （SVM） classifier were combined to realize the automatic recognition of ADHD. The speech samples of the experiment were collected from 17 normal control children and 37 children with ADHD. Experimental results show that the proposed algorithm can effectively discriminate the normal children and children with ADHD， with an accuracy of 91.38%.

Knowledge Graph （KG） can effectively extract information by efficiently organizing massive data. Therefore， recommendation methods based on knowledge graph have been widely studied and applied. Aiming at the sampling error problem of graph neural network in knowledge graph modeling， a method of Non-sampling Collaborative Knowledge graph Network （NCKN） was proposed. Firstly， a non-sampling knowledge dissemination module was designed， in which linear aggregators with different sizes were used in a single convolutional layer to capture deep-level information and achieve efficient non-sampling pre-computation. Then， in order to distinguish the contribution degrees of neighbor nodes， attention mechanism was introduced in the dissemination process. Finally， the collaboration signal of user interaction and knowledge embedding were combined in the collaborative dissemination module to better describe user preferences. Based on three real datasets， the performance of NCKN in CTR （Click Through Rate） prediction and Top-k was evaluated. The experimental results show that compared with the mainstream algorithms RippleNet （Ripple Network） and KGCN （Knowledge Graph Convolutional Network）， the accuracy of NCKN in CTR prediction increases by 2.71% and 4.60%， respectively； in the Top-k forecast， prediction， the accuracy of NCKN increases by 5.26% and 3.91% on average respectively. The proposed method not only solves the sampling error problem of graph neural network in knowledge map modeling， but also improves the accuracy of the recommended model.