Optimization of long short-term memory network algorithm for fake news detection and classification based on multi head attention mechanism

In order to effectively resist the interference of fake news on data quality in consultation summary generation and knowledge search scenarios, we adopted a machine learning based fake news detection and classification method. The core of this article adopts an optimized long short-term memory network algorithm, which integrates multi head attention mechanism and aims to improve the recognition ability of fake news. In the early stages of our research, we first carried out text feature extraction, which laid the foundation for subsequent classification and detection by extracting feature information that helps the model make accurate judgments. Subsequently, we introduced different datasets (training set, validation set, and testing set) into the model for training and evaluation. During the training process, we observed that the loss value of the model showed a gradually decreasing trend and tended to stabilize, while the accuracy showed signs of continuous increase, ultimately reaching a relatively stable level. These performances fully demonstrate that the model has achieved satisfactory results in predicting fake news. In addition, compared to the accuracy difference of only 1.29% between the training and testing sets, this phenomenon further validates the model's good generalization ability, meaning that it is not only effective on the training data, but also able to adapt and accurately process new data. In summary, the long short-term memory network model based on multi head attention mechanism optimization proposed in this article demonstrates excellent fake news detection and classification performance, and has high application value and practical significance. This achievement has laid a foundation for resisting the spread of fake news, improving information quality, and promoting healthy information ecosystem.