Time-of-flight secondary ion mass spectrometry (ToF-SIMS) is an important analysis technique that can gather vast amounts of information from surfaces. Recently, machine learning was combined with ToF-SIMS to successfully extract useful information from mass spectra. However, the descriptor generation required for ToF-SIMS analysis using machine learning remains challenging because it requires a lot of effort, is time-consuming, and significantly limits the versatility and practicality of the machine learning approach for ToF-SIMS analysis. Herein, we proposed a new approach to avoid the descriptor generation: to regard ToF-SIMS spectra as images and apply the convolutional neural network (CNN) to analyze these spectral images. We applied and assessed this approach for the identification of silane coupling agents in multicomponent films. Furthermore, the CNN showed higher accuracy than descriptor-based approaches, suggesting its usefulness in achieving the automation and standardization of the ToF-SIMS analysis.