A method for modeling the learning of belief combination in evidential reasoning using a neural network is presented. A centralized network composed of multiple exponential bidirectional associative memories (eBAM's) sharing a single output array of neurons is proposed to process the uncertainty management of many pieces of evidence simultaneously. The stability of the proposed multiple eBAM network is proved. The sufficient condition to recall a stored pattern pair is discussed. Most important of all, a majority rule of decision making in presentation of multiple evidence is also found by the study of signal-noise-ratio of multiple eBAM network. A guaranteed stable state condition, i.e., the condition for the fastest recall of a pattern pair, is also studied. The result is coherent with the intuition of reasoning.