With the development of artificial intelligence, Internet of things, machine learning, and many other technologies, animation design task based on algorithm theory has become a research hotspot in the field. In recent years, perception technology has gradually become the key technology of animation design, and it is also the key research content in the current field. Whether the perception system can design animation quickly and accurately is the key of research. Compared with other algorithms, using AVOD (Aggregate view object detection) algorithm for animation design has obvious advantages. The original AVOD algorithm has some problems, such as low clustering efficiency, insufficient depth of feature extraction network, and occupying a large amount of memory. Based on this, this paper proposes to use the googleNet network and initial model of k-means++ to extract features and establish an optimized AVOD algorithm. At the same time, in order to illustrate the effectiveness of the optimization method, two typical cases are introduced to provide scientific guidance and reference for the research in this field.

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