A video conversion method comprising the steps of: performing labeling for annotations in a 3D manual for a cloud service and machine learning for recognizing the annotations; automatically recognizing the annotations, fine-adjusting automatically unrecognized annotations, and generating a file for converting the 3D manual into a 2D interactive video; linking the 3D manual to a corresponding 2D manual using the file; extracting and handling artificial intelligence knowledge based on work instructions in the 2D manual; and performing neural network tasks and linguistic inferences for processing voice requests and informing the user about the handling and completion of a requested task by sending visual and audio feedback, in order to execute commands based on the artificial intelligence knowledge, the digital twin, and the annotations in the 3D manual. The video conversion method of claim 1, wherein, in the step of performing labeling for annotations in a 3D manual for a cloud service and machine learning for recognizing the annotations by means of a learning part, the positions of annotation objects in the 3D manual are represented in a coordinate system, and labeling for annotations in the 3D manual is performed so as to automatically recognize the positions of the annotations, and, after the labeling, machine learning is performed to automatically recognize annotations on objects in the video through an object detection model. The video conversion method of claim 1, wherein, in the step of automatically recognizing the annotations, fine-adjusting automatically unrecognized annotations by means of a video editor, and generating a file for converting the 3D manual into a 2D interactive video, the automatically unrecognized annotations are adjusted by adjusting the positions and shapes of the annotations and then adjusting the durations of the annotations by means of the video editor, and the automatically recognized annotations represent a list of taks for interaction, including object information, parts information, marks for proceeding to a next task or returning to a previous task, parts positions, relevant videos, and caution and warning buttons. The video conversion method of claim 1, wherein, in the step of linking the 3D manual to a corresponding 2D manual using the file by means of a 2D manual linking part, the 3D manual is linked to the corresponding 2D manual using the file which includes the positions of 2D drawings in a video, object label names, the start and end times of bounding boxes for interaction, and the positions of the bounding boxes in a video frame, and a corresponding interaction is performed when an annotation in the video is clicked. A video conversion apparatus comprising: a processor; a memory coupled to the processor, the memory containing instructions, that when executed by the processor, perform the steps of: performing labeling for annotations in a 3D manual for a cloud service and machine learning for recognizing the annotations; automatically recognizing the annotations, fine-adjusting automatically unrecognized annotations, and generating a file for converting the 3D manual into a 2D interactive video; linking the 3D manual to a corresponding 2D manual using the file; extracting and handling artificial intelligence knowledge based on work instructions in the 2D manual; and performing neural network tasks and linguistic inferences for processing voice requests and informing the user about the handling and completion of a requested task by sending visual and audio feedback, in order to execute commands based on the artificial intelligence knowledge, the digital twin, and the annotations in the 3D manual. The video conversion apparatus of claim 5, wherein the labeling represents the positions of annotation objects in the 3D manual in a coordinate system and performs labeling for annotations in the 3D manual so as to automatically recognize the positions of the annotations, and, after the labeling, performs machine learning to automatically recognize annotations on objects in the video through an object detection model. The video conversion apparatus of claim 5, wherein the automatically recognizing adjusts the automatically unrecognized annotations by adjusting the positions and shapes of the annotations and then adjusting the durations of the annotations, and the automatically recognized annotations represent a list of taks for interaction, including object information, parts information, marks for proceeding to a next task or returning to a previous task, parts positions, relevant videos, and caution and warning buttons. The video conversion apparatus of claim 5, wherein the linking links the 3D manual to the corresponding 2D manual using the file which includes the positions of 2D drawings in a video, object label names, the start and end times of bounding boxes for interaction, and the positions of the bounding boxes in a video frame and performs a corresponding interaction when an annotation in the video is clicked.