Modern agricultural equipment is developing towards the intelligent machinery, and deep learning and machine vision are core technologies in realizing the intelligent machinery. In terms of the machinevision based intelligent agricultural machinery that works in maize field, they should forward towards at the maize stem or avoiding the maize stem, while the ability to identify the maize stem accurately is the premise to ensure them work properly. Aiming to distinguish the maize (Zea Mays L.) stems, which grew in the field. In order to acquire the high quality field pictures, one chargecoupled device (CCD) camera was mounted in one camera gimbal. The plugin unit named after LabelImage was applied to mark and label the maize plants, based on the deep learning framework TensorFlow, a convolution neural network model with multiscale hierarchical features was built, and the unit convolution kernel with four times expansion was applied. Thus the maize seedling recognition model was obtained with the recognition accuracy of 9965%. Based on the recognition results, the morphological process was conducted by the OpenCV 342 and the Python 365. The threshold value exerted the major influence on depending the information completeness during the binary process, the pictures that contained the maize seedlings were divided into an optimum parts, then an optimum threshold value for each part would be calculated by the algorithm that described. Inspired by the bounding rectangle of each object was different in the binary picture, the aspect ratio was utilized to distinguish the maize seedling stem, and the minimum aspect ratio was computed, then the corresponding bounding rectangle was filled red which indicated the stems. The field experiment was conducted from June 20th to June 22nd 2018, and totally 10800 pictures were shot during these three days. Five shooting times and six kinds of weed densities were took into consideration for each day. The experimental results showed that the mean identification accuracy was 9893%, and neither the shooting times (P>0.05) nor the weed densities (P>0.05) had significant influence on the recognition accuracies. The research result had applicable value, and it can be used as the upstream technology for the intelligent agricultural equipment.