In order to strengthen the implied quality and safety risk management of aquatic products cold chain enterprises on internal control data and realize cost reduction and efficiency, taking the HACCP plan of raw oysters as an example, the quality and safety risk warning indicator system was constructed from the construction of the risk indicator system and combined with the multimodal characteristics of the risk warning monitoring data to construct the quality and safety fusion of group experts-domain empirical knowledge and deep learning algorithms. On the basis of designing quality and safety early warning data collection points and obtaining monitoring data based on the HACCP plan, the AHP method was used to obtain the utility value scheme of the experts-risk indicators, and the entropy weight method was used to optimize the utility value scheme of multi-expert group decision-making, and then the rating data was determined, which constituted the original dataset from the monitoring data as well as the optimized expert rating data. In order to ensure the sensitivity of the early warning, the qualified dataset and the complete dataset were adopted as the dataset, and the LSTM model with the ability to capture the complex relationships in the multidimensional data and the RBF model with the ability to deal with the complex classification boundaries were fused to construct the early warning model to carry out the simulation experiments, and to do the comparative analyses between the LSTM model, the RBF model, and the fused LSTM-RBF model on the different datasets. The experimental results showed that the fused LSTM-RBF model had 96% and 90% accuracies on both qualified and complete datasets, and the test results on qualified datasets were significantly better.