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In the field of mechanical processing, hole processing accounts for one-third of the total, and deep-hole processing accounts for more than half of the hole processing. Intelligent condition monitoring can realize real-time perception of the state of the equipment in the processing process, guide the adjustment of processing parameters, optimize the product quality, and give a timely warning when the equipment life is insufficient or failure occurs. Intelligent monitoring in machining is a key technical link to realize intelligent manufacturing. The intelligence of cutting is of great significance to the intelligence of the whole manufacturing industry. A variety of typical deep learning models are introduced for performance comparison, which proves the superiority of the models and methods used in this paper. The final classification accuracy is 91.2%. The matrix is input into the model to recognize the boring bar’s vibration state. The original signals are transformed into a 256 × 256 × 3 matrix obtained by a two-dimensional time–frequency spectrum diagram. The three-way acceleration and sound pressure signals are collected, and the signals are processed by smoothed pseudo-Wigner–Ville distribution. The boring experiment platform is built, and 192 groups of cutting experiments are carried out. Based on grouping convolution, channel shuffle, and BiLSTM, a shuffle-BiLSTM NET model is constructed, which is both lightweight and has a high classification accuracy. In this paper, the boring bar is taken as the research object, and an intelligent monitoring technology of the boring bar’s vibration state based on deep learning is proposed. To detect the change of the vibration state of the boring bar over time, guide the adjustment of the processing parameters, and avoid wastage of the workpiece and the loss of equipment, it is particularly important to intelligently monitor the vibration state of the boring bar during processing. It is often inaccurate and inefficient to judge the vibration state of the boring bar through artificial experience. Due to its low stiffness, the boring bar used in deep-hole-boring is prone to violent vibration during the cutting process.