Nuclear magnetic resonance (NMR) is an indispensable tool for structural elucidation and noninvasive analysis. Automated identification of analytes with NMR is highly pursued in metabolism research and disease diagnosis; however, this process is often complicated by the signal overlap and the sample matrix. We herein report a detection scheme based on 19F NMR spectroscopy and dynamic recognition, which effectively simplifies the detection signal and mitigates the influence of the matrix on the detection. It is demonstrated that this approach can not only detect and differentiate capsaicin and dihydrocapsaicin in complex real-world samples but also quantify the ibuprofen content in sustained-release capsules. Based on the 19F signals obtained in the detection using a set of three 19F probes, automated analyte identification is achieved, effectively reducing the odds of misrecognition caused by structural similarity.