In this work, we demonstrate a DL algorithm for automatic mechanistic analysis for cyclic voltammetry (Fig. ). State-of-art DL algorithms using residual neural networks (ResNet) architecture are established to analyze cyclic voltammograms at different scan rates {v, i(E)} and yield the electrochemical system's probability towards five of the most common stoichiometric homogenous mechanisms in electrochemistry textbooks (Fig. ): 1,2,15,16 a single reversible electron transfer (Er), an Er step followed by a reversible C step (ErCr), an Er step preceded by a Cr step (CrEr), a system of two Er steps connected by an irreversible rate-limiting Ci step with the second Er step being more thermodynamically facile than the first one (ECE), and a two-electron transfer that is similar to ECE yet the second Er step is replaced by a solution disproportionation reaction (DISP1). We demonstrate DL's capability of accurately designating mechanisms in simulated and experimental scenarios, unveiling potential new features in the voltammograms elusive to manual inspection, as well as semi-quantitatively observing the gradual transitions of electrochemical mechanisms. The developed algorithm will be applicable to analyze complex electrochemical systems when competing mechanisms are intertwined together. In conjunction with robotic experimentation, the demonstration of automatic mechanistic analysis in cyclic voltammetry presents the possibility of automated high-throughput research to investigate mechanisms in electrochemical systems with minimal human intervention.