Objective To construct a detection and prediction model for suspected respiratory infectious diseases (RIDs) based on clinical data center, and achieve the detection and prediction of suspected infectious diseases. Methods Clinical data were selected from a tertiary first-class hospital, structural modeling of medical records was constructed based on historical data of infectious diseases, knowledge map of RIDs was formulated, a combined decision model of detection and prediction was formed through XGboost algorithm and knowledge map reasoning technology, and cross validation based on hospital historical data was performed, a model with high accuracy was obtained. Results The average precision ratio of the detection and prediction model was 92.55%, with recall ratio of 91.49% and the comprehensive F1 test value of 92.01%, which were superior to the individual knowledge map model or XGboost model. The model was integrated with the hospital's electronic medical record system and clinical decision support system for predicting real clinical cases. Conclusion This method can effectively predict emerging suspected RIDs, assist hospitals to initiate emergency plans timely for infectious diseases, and reduce the probability of infection among health care workers at the early stage of infectious diseases.