With the proliferation of online medical communities, vast amounts of valuable medical and health question-and-answer data have emerged, paving new avenues for the extraction of public health information and the enhancement of question-and-answer system efficiency. To classify public health-related questions more accurately, this paper introduces a novel model ensemble approach that skillfully integrates the advantages of BERT and two large language models, aiming to achieve precise categorization of public health questions by aggregating predictions from each model. To validate the effectiveness of this method, we conducted detailed experiments using the Chinese Medical Intent Dataset. The proposed method was then compared with various existing methods, showing a significant improvement in accuracy, demonstrating the outstanding efficacy of combining model integration with a voting mechanism in the classification task of public health questions.