Abstract: 【Purpose/significance】In order to promote the accurate recommendation of online health community Q & A and boost the
high-quality development of digital medical services, this paper proposes the information adoption forecasting model according to the
stacking ensemble strategy which based on the massive online health community Q & A information.【Method/process】The stacking
ensemble strategy chooses non-integrated learning method and integrated learning method as the first layer learners, while linear re?
gression is used as the meta learner. We choose 'xywy.com' to build the dataset and construct predict indicators, including text struc?
ture, online social communication record, professional authority. We compare the prediction accuracy between single prediction model
and stacking ensemble strategy with different model combination. Then we select the data from '120ask.com' platform to verify the gen? eralization of stacking ensemble strategy.【Result/conclusion】The results demonstrate that the stacking ensemble strategy has higher prediction accuracy and strong generalization than the single prediction model, which can be applied to different online health Q & A communities.【Innovation/limitation】Based on machine learning methods, the stacking ensemble strategy can significantly improve the prediction accuracy of information adoption for online health Q & A communities. At the same time, the communication patterns are changing in online health Q & A communities, and it is important to take the daily updated data into account to improve the predict accuracy by stacking ensemble strategy in the future research.