Disclosed is a VOD service cache replacement method based on a random forest algorithm in an edge network environment. The method comprises the following steps: collecting video data; processing a missing value of the video data using a random forest filling method, and establishing a prediction model; predicting an average access duration by means of the prediction model; establishing a cache replacement model according to a prediction resu and solving the cache replacement model using an implicit enumeration method to obtain a final replacement scheme. According to the present invention, an edge server needing to process a large amount of video information and machine learning having an excellent analysis capability in terms of big data processing are taken into consideration, and a random forest algorithm in machine learning is first used to predict a weekly average access duration for a video. Therefore, on this basis, a new video cache replacement model is provided, and the model is solved using an implicit enumeration method, such that the load of a core network is reduced to the greatest extent by an edge server. Moreover, the scheme is very simple and is easily implemented, and has very good application prospects.
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