A method and system for identifying an electronic component polarity direction, and a method and system for marking the electronic component polarity direction. The identification method comprises: first, acquiring an image containing an electronic component (S101); then performing a forward calculation on the image by using a trained convolutional neural network, so as to obtain a probability distribution of polarity direction categories of the electronic component (S102); and selecting a polarity direction category having a maximum probability as the polarity direction category of the electronic component (S103). In the method, a convolutional neural network is used, and accordingly polarity directions of electronic components can be identified automatically and precisely; the method is not aimed at a specified electronic component structure but is suitable for all kinds of electronic components having polarity, and accordingly cross-category polarity direction identification of electronic components is imp
