Associating form widgets with an urban knowledge system to transform form filling based data into a standardized urban knowledge graph automatically presents a novel solution to the challenge of large-scale data element production. However， the form system integrates numerous widgets named using domain-specific terms such as entities， relationships， and their attributes， making it difficult for users to find the desired widget quickly， thereby giving rise to the task of form widget recommendation. To tackle three key challenges： significant variability in application scenarios， complex contextual dependencies in configuration， and sparse form configuration data， a Tree-structured Long Short-Term Memory （Tree-LSTM） network based Recommendation model for Form Widget （TRFW） was proposed. Firstly， a scenario multi-classification model was trained on rich and general form configuration data to learn dependencies between form texts and scenario features. Then， a network structure of Tree-LSTM was employed to extract structural and thematic features from form contexts， and the user configuration intent features were constructed on this basis. Concurrently， an AutoEncoder （AE）-based widget naming encoder was trained to establish loosely-coupled connections between semantically similar widget names， thereby enhancing the model’s ability to recommend semantically relevant widgets while maintaining robustness for newly added widgets. Experimental results on a publicly sourced dataset demonstrate that the proposed model has superior performance compared to all baseline models， achieving a HitRatio@1 of 84.62% and a HitRatio@10 of 97.31%， with a minimum improvement of 87.0% in HitRatio@1 over unsupervised methods， while outperforming graph convolution-based recommendation methods by at least 32.4% and 12.4% in HitRatio@1 and HitRatio@10， respectively. It can be seen that the proposed model can recommend widget lists that align with user intent effectively， thereby reducing user interaction costs during widget selection significantly.