Significance Fluorescent biosensors are usually designed to recognize a single target analyte and provide a one-dimensional signal from an emission spectrum. Higher-dimensional information in emission spectra and latent factors remain insufficiently utilized. Here we report a broad-spectrum fluorescent biosensor and a general methodology to evaluate spectral shape recognition to classify biomolecules using machine learning. Using a feature selection algorithm to measure the relative intensity of a few selected wavelengths significantly reduces the measurement time, demonstrating the potential for fluorescence spectrum shape analysis in high-throughput technologies. By using well-defined analytes, we explain the mechanism of these fluorescence spectral shape changes, which is fundamental for applying this method for deeper insight into complex phenomena with correlated signals in biological systems.