Dempster-Shafer证据理论是一种用于不确定性建模与推理的理论框架，其中证据建模是关键环节之一。现有证据建模方法各有优劣，如能综合利用则有望达到更优的建模效果。显式地使用多种证据建模方法再融合的效率较低，因此本文提出了一种基于深度学习的多方法联合端到端证据建模方法。通过训练一个深度网络，学习从训练样本特征到作为广义训练标签的融合证据函数的映射关系，以此实现多方法联合端到端证据建模。在UCI数据集、遥感图像数据集上的实验结果表明，提出的证据建模方法相比于对比的单一证据建模方法，可以达到更优的分类性能。

Dempster-Shafer evidence theory (DST) is a theoretical framework for uncertainty modeling and reasoning, with modeling the basic belief assignment (BBA) as one of its most crucial and challenging steps. The prevailing BBA determination methods have their own pros and cons, and the joint use of them is expected to provide a better BBA. Explicitly using several BBA determination methods and combining the BBAs through a specific fusion rule is inefficient. Therefore, a learning-based BBA modeling approach with multi-method fusion is proposed in this paper. A deep network is trained which learns the mapping from the training samples to the comprehensive BBAs obtained by jointly using the prevailing BBA modeling methods as the generalized training labels. Experimental results on remote sensing image datasets and UCI datasets demonstrate that the proposed method outperforms the individual BBA modeling methods in terms of classification performance.