Aiming at the problems of slow transcoding speed of single-machine video transcoding method and limited efficiency improvement of parallel transcoding method for batch processing, a fast video transcoding method for stream processing based on Spark Streaming distributed stream processing framework was proposed. Firstly, an automated video slicing model was built by using the open source multimedia processing tool of FFmpeg and a programming algorithm was proposed. Then, in view of the characteristics of parallel video transcoding, the stream processing model of video transcoding was constructed by studying Resilient Distributed Datasets (RDD). Finally, the video merging scheme was designed to store the combined video files effectively. Based on the proposed fast video transcoding method, a fast video transcoding system based on Spark Streaming was designed and implemented. The experimental results show that, compared with the Hadoop video transcoding method for batch processing, the proposed method has improved the transcoding efficiency by 26.7%, and compared with the video parallel transcoding based on Hadoop platform, the proposed method has improved the transcoding efficiency by 20.1%.