Electrochemical analysis for mechanistic investigation has relied heavily on manual inspection, which demands extensive training and may be prone to human bias and misinterpretation due to researchers' prior experience. In this work, we demonstrated a DL algorithm based on ResNet architecture that automatically analyzes cyclic voltammograms and, congruent with manual inspection, suggests the most probable mechanism among five of the most common mechanisms in homogenous molecular electrochemistry. Potentially being more sensitive and capable of detecting subtle elusive features, the established DL model can also semi-quantitatively analyze competing pathways and observe the gradual transition from one mechanism to another. The established DL algorithm will be further refined with experimental data, being specifically targeted for algorithm development or contributed to by the general electrochemistry community via an open-access online platform. Efforts of expanding the types of electrochemical mechanisms, including homogenous, heterogeneous, stoichiometric and catalytic transformations, will advance the utility of the algorithm. In the long run, this DL-based approach will aid if not replace extensive manual mechanistic inspections in electrochemistry. We propose that such automatic analysis will find its advantages of analyzing complex reaction schemes that may be beyond the capacity of manual analysis, such as the square diagrams with the possibility of concerted pathways in systems of proton-coupled electron transfer. The semi-quantitative output of the developed DL algorithm also offers a mathematically quantified feature, which can be the subject of Bayesian optimization that seeks to maximize electrochemical transformations with optimal experimental conditions. In conjunction with automatic robotic experimentation, an autonomous high-throughput electrochemistry research will become feasible, where Bayesian optimization strives to maximize certain transformations with intelligently varied experimental conditions (e.g. reactant type and concentrations) in an iterative fashion, by "learning" the parameter space via our DL algorithm and deciphering the partition of various reaction pathways measured in cyclic voltammetry.