A classification prediction algorithm based on Long Short-Term Memory Network (LSTM) improved AdaBoost is used to predict virtual reality (VR) user experience. The dataset is randomly divided into training and test sets in the ratio of 7:3.During the training process, the model's loss value decreases from 0.65 to 0.31, which shows that the model gradually reduces the discrepancy between the prediction results and the actual labels, and improves the accuracy and generalisation ability.The final loss value of 0.31 indicates that the model fits the training data well, and is able to make predictions and classifications more accurately. The confusion matrix for the training set shows a total of 177 correct predictions and 52 incorrect predictions, with an accuracy of 77%, precision of 88%, recall of 77% and f1 score of 82%. The confusion matrix for the test set shows a total of 167 correct and 53 incorrect predictions with 75% accuracy, 87% precision, 57% recall and 69% f1 score. In summary, the classification prediction algorithm based on LSTM with improved AdaBoost shows good prediction ability for virtual reality user experience. This study is of great significance to enhance the application of virtual reality technology in user experience. By combining LSTM and AdaBoost algorithms, significant progress has been made in user experience prediction, which not only improves the accuracy and generalisation ability of the model, but also provides useful insights for related research in the field of virtual reality. This approach can help developers better understand user requirements, optimise virtual reality product design, and enhance user satisfaction, promoting the wide application of virtual reality technology in various fields.

翻译：本文采用一种基于长短期记忆网络（LSTM）改进AdaBoost的分类预测算法来预测虚拟现实（VR）用户体验。数据集按7:3的比例随机划分为训练集和测试集。训练过程中，模型损失值从0.65降至0.31，表明模型逐步减小了预测结果与实际标签之间的差异，提升了准确率与泛化能力。最终损失值0.31说明模型对训练数据拟合良好，能够更准确地进行预测与分类。训练集的混淆矩阵显示共有177项正确预测与52项错误预测，准确率为77%，精确率为88%，召回率为77%，F1分数为82%。测试集的混淆矩阵显示共有167项正确预测与53项错误预测，准确率为75%，精确率为87%，召回率为57%，F1分数为69%。综上所述，基于LSTM改进AdaBoost的分类预测算法对虚拟现实用户体验展现出良好的预测能力。本研究对提升虚拟现实技术在用户体验领域的应用具有重要意义。通过结合LSTM与AdaBoost算法，在用户体验预测方面取得了显著进展，不仅提高了模型的准确率与泛化能力，也为虚拟现实领域的相关研究提供了有益参考。该方法可帮助开发者更好地理解用户需求，优化虚拟现实产品设计，提升用户满意度，推动虚拟现实技术在各个领域的广泛应用。