With the development of experiential tourism and the improvement of people’s living standards, people have begun to transform tourist destinations into museum tourism. However, no effective method for predicting the demand for museum tourism has yet emerged. In order to be able to build a prediction model that can perceive the needs of museum tourists, this article uses advanced algorithms based on neural network integration and calls different algorithms: QPSO-BPNN, QPSO, PSO, PSO-BPNN, and BPNN. When the training ratio increases to 90%, the prediction accuracy of the three algorithms, BPNN, PSO, and PSO-BPNN, is less than 80%, and the prediction accuracy of the QPSO-BPNN algorithm has reached 92.5%. Under the condition of equal training set ratio, the prediction accuracy of QPSO-BPNN algorithm is always significantly higher than that of PSO-BPNN algorithm. When the training set proportions are 50%, 70%, and 90%, the changes in population size parameters have little effect on the prediction accuracy of the algorithm. Based on the above experiments, it is known that the QPSO-BPNN algorithm is less sensitive to the size of the population, and the algorithm has good robustness. With the increase in the number of initial classifiers, the prediction accuracy of the QPSO-BPNN algorithm has improved significantly. The experimental results are consistent with the previous theoretical derivation analysis, and the accuracy of the algorithm has a positive correlation with the number of classifiers.

随着体验式旅游的发展和人们生活水平的提高，人们已经开始将旅游目的地转变为博物馆旅游。但是，尚未出现预测博物馆旅游需求的有效方法。为了能够建立一个可以感知博物馆游客需求的预测模型，本文使用了基于神经网络集成的高级算法，并调用了不同的算法：QPSO-BPNN，QPSO，PSO，PSO-BPNN和BPNN。当训练率提高到90％时，BPNN，PSO和PSO-BPNN三种算法的预测精度均低于80％，QPSO-BPNN算法的预测精度达到92.5％。在训练集比率相等的情况下，QPSO-BPNN算法的预测精度始终明显高于PSO-BPNN算法。当训练集比例为50％，70％和90％时，种群大小参数的变化对算法的预测准确性影响很小。根据以上实验，可以知道QPSO-BPNN算法对种群的大小较不敏感，并且算法具有很好的鲁棒性。随着初始分类器数量的增加，QPSO-BPNN算法的预测精度得到了显着提高。实验结果与先前的理论推导分析相吻合，算法的准确性与分类器数量成正相关。种群大小参数的变化对算法的预测精度影响很小。根据以上实验，可以知道QPSO-BPNN算法对种群的大小较不敏感，并且算法具有很好的鲁棒性。随着初始分类器数量的增加，QPSO-BPNN算法的预测精度得到了显着提高。实验结果与以往的理论推导分析结果吻合，算法的准确性与分类器数量成正相关。种群大小参数的变化对算法的预测精度影响很小。根据以上实验，可以知道QPSO-BPNN算法对种群的大小较不敏感，并且算法具有很好的鲁棒性。随着初始分类器数量的增加，QPSO-BPNN算法的预测精度得到了显着提高。实验结果与先前的理论推导分析相吻合，算法的准确性与分类器数量成正相关。随着初始分类器数量的增加，QPSO-BPNN算法的预测精度得到了显着提高。实验结果与以往的理论推导分析结果吻合，算法的准确性与分类器数量成正相关。随着初始分类器数量的增加，QPSO-BPNN算法的预测精度得到了显着提高。实验结果与以往的理论推导分析结果吻合，算法的准确性与分类器数量成正相关。