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.

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.