Online social media are frequently used by people as a way of expressing their thoughts and feelings. Among the vast amounts of online posts, there may be more concerning ones expressing potential grievances and mental illnesses. Identifying these along with potential causes of mental health problems is an important task. Observing these posts, it is found that there is a label co-occurrence phenomenon in contexts, i.e., the semantics of multiple candidate labels appear in the context of one sample, which interferes with the modeling and prediction of label patterns. To mitigate the impact of this phenomenon, we propose a label-aware data augmentation method, which leverages large-scale pre-trained language models with excellent text comprehension capability to identify potential candidate labels, abates the noise from irrelevant co-occurring labels by estimating sample-independent label semantic strengths, and constructs well-performing classifiers with pre-trained language models. Extensive experiments validate the effectiveness of our model on the recent datasets Intent_SDCNL and SAD.