The difficulty of relying on historical interaction data for effective modelling in the cold-start stage of recommender systems constrains the quality and precision of personalized services severely. To address this issue， user profile pictures and nicknames obtained from login recommender systems were utilized， and a multimodal identification model combining user profile pictures and nicknames， ResBERT-MMNet （ResNet-BERT MultiModal Network）， was proposed. In the model， a multi-layer stacked bidirectional residual cross-modal attention mechanism was designed to achieve image-text semantic interaction， thereby enhancing the understanding of user identities. Furthermore， to address the limitation of incorrect predictions produced by multimodal models under low-confidence conditions， a Large Language Model （LLM）?based prediction of agricultural user identity types was introduced， and the DeepSeek-R1-Distill-Qwen-7B model was used for Low-Rank Adaptation （LoRA） fine-tuning， so as to infer user identity types from user nicknames， and the calculation results of LLM were employed as a supplementary decision-making module to improve overall system robustness. Experimental results on a self-built dataset demonstrate that ResBERT-MMNet outperforms other models in multi-type agricultural user identification tasks with the identification precision and F1-score reaching 67.47% and 68.05%， respectively. After integrating with the fine-tuned LLM， the model achieves an identification precision of 69.73% and an F1-score of 70.06%， indicating strong practical applicability. It can be seen that the proposed model provides the prior data in the cold-start stage of agricultural recommender systems and offers a technical solution for user profiling and precision services.