Rhinitis is a common chronic inflammation of the upper respiratory tract with a variety of symptoms and signs. The clinical classification of rhinitis is characterized by different types of instances and class imbalance， and belongs to multiple output classification. Low recognition rate and poor generalization performance often occur for minority class instances. Therefore， this article proposes a novel classification model based on heterogeneous integrated frame， which translates the multi-output classification of rhinitis to multi-label and multi-class classification， then builds a heterogeneous integrated classifier by ensemble learning algorithm. The proposed model can automatically adjust the number and depth of integrated forest learners according to the imbalance ratio of single class label in a subset. As a result， it can effectively reduce influence of class imbalance and improve classification performance of majority and minority class concurrently， further to enhance generalization of integrated classifiers. We conduct cross-validation classification experiments on 461 cases of clinical rhinitis. The outcomes show that the evaluation indicators of the proposed model， such as sensitivity， specificity， accuracy， F1 and AUC， are 74.9%，86.5%，92.0%，0.783 and 0.953， respectively. In comparison to other baseline methods， it achieves better evaluation performance and is more suitable for the early clinical diagnosis of rhinitis.