Abstract: Aiming at the problems that, in the prediction and analysis of public-opinion information spreading based on mean-field epidemic models, it is difficult to iteratively correct parameters within the model itself, which can lead to prediction bias, and that the LSTB model shows poor long-term prediction accuracy for public-opinion information propagation, the SEI³R-BiLSTM model integrating communication dynamics and deep learning technology was proposed. Firstly, the SEIR model was improved by classifying user states during the dissemination of online public opinion information into six categories: S (Information Unaware), E (Information Hesitant), I₁ (Positive Communicator), I₂ (Negative Communicator), I₃ (Neutral Communicator), and R (Information Immune), with clear definitions of the transition relationships between these states. Secondly, to enhance the model’s accuracy, the attention mechanism and residual connection were introduced by combining the BiLSTM neural network model, enabling the prediction of changes in the number of public opinion information communicators. Finally, 659,000 posts were collected from Sina Weibo across three high-profile public opinion events, including "the Jiang Ping Mathematics Competition", "Qin Lang Losing Homework", and "Fat Cat Jumping into the River", for experimental validation and analysis. The results showed that the time-series curves of the number of three types of communicators (I₁, I₂, and I₃) predicted by the SEI³R-BiLSTM model were generally consistent with the actual propagation trends, with high fitting accuracy. Furthermore, the performance of SEI³R-BiLSTM model was better than the four models including SEI³R-LSTM and SEI³R-ARIMA, based on four evaluation metrics including RMSE (0.162), MAPE (16.6%), Jaccard (0.74), and F1 score (0.72). In addition, the ablation experiment further confirmed the model’s rationality and effectiveness. These findings provide a model reference for predicting the development of online public opinion.

摘要： 针对依据平均场理论的传染病模型预测分析舆论信息传播中，难以通过模型自身迭代修正参数，易导致预测偏差，以及LSTB模型对舆论信息传播长期预测精度较差等问题，提出一种融合传播动力学与深度学习技术的SEI³R-BiLSTM模型。首先，改进SEIR传播模型，划分网络舆论信息传播过程中用户状态为S（信息未知者）、E（信息犹豫者）、I1（正面传播者）、I2（负面传播者）、I3（中立传播者）、R（信息免疫者）6类，设置各状态间的转换关系；然后，结合BiLSTM神经网络模型，引入注意力机制、残差连接等提升模型精度，预测舆论信息传播者数量变化；最后，采集“姜平数学竞赛”、“秦朗丢作业”和“胖猫跳江”3个热点舆论事件，共65.9万条微博平台舆论数据，进行实验验证与分析。结果表明，SEI³R-BiLSTM模型预测I₁、I₂、I₃三类传播者数量时序变化曲线与实际传播走势整体上一致，拟合精度高；依据RMSE（0.162）、MAPE（16.6%）、Jaccard(0.74) 、F1-score值(0.72) 4个评估指标数值，SEI³R-BiLSTM模型性能优于SEI³R-LSTM、SEI³R-ARIMA等4个融合型基准模型；消融实验部分证明了模型的合理性与有效性，为预测网络舆情发展提供了一种模型参考。