In the laser driven inertial confinement fusion experiments, several dozens of diagnostic instruments are needed, located in different sightlines of view. Most of the instruments are required to work inside the giant vacuum target chamber, in distances of centimeters to meters. They are always moved from several meters away, by the general diagnostic instrument manipulator and point the micro target in a precision of about 50 μm. The binocular pointing method is one of the continual alignment methods, which are able to work in the vacuum and in a faraway distance. However, at present this method needs target recognizing by eyes and pointing in a manual operation. Under some situations, such as under low illumination or pointing sightlines with an angle, the target marker may not be accurately recognized, as a result, the precision of the diagnostic alignment degenerates rapidly. In this work, a machine vision aided autonomic alignment method is proposed. The Mask R-CNN algorithm is used to recognize the target marker. Many simulated visual target graphs are created to train the algorithm. The accuracy of the target marker recognizing increases obviously. The error in the test is less than 8 pixels. Furthermore, the relation between the pixel shift in the visual graph and the pointing shift in the coordinate is calibrated in the laboratory. According to the machine vision aided alignment method, the tested alignment precision in pointing direction is estimated to be less than 30 μm, and in axial direction less than 50 μm. With the feedback of the alignment shift, the diagnostic alignment is able to be in an autonomic operation.