A multi-direction bending sensor based on spot pattern demodulation of a dual-hole fiber (DHF) is proposed. By using the interference and scattering in a DHF, the related multidirectional variations can be captured by the optical field. Furthermore, the multi-directional bending characteristics of the fiber are quantitatively described by the pattern of the output light spot, achieving multidirectional bending sensing. In addition, considering the subtle changes in the deformation patterns over time, a convolutional neural network (CNN) model based on deep learning is introduced for accurate recognition and prediction of the bending angle. The experimental results show that the sensor can perceive different bending angles in four directions. These outstanding results indicate that the multi-directional bending sensor based on dual-hole interference pattern decoding has potential applications in multi-directional quantitative sensing and artificial intelligence perception.