Based on the classical smoothing technology, this paper proposes a smoothing approach within head-driven parsing, which directly calculates interpolation weight from the average occurrences of event in the training sample and is proved by the statistic theory of errors. By using this approach and deriving zero-value assumption from other smoothing technologies, this paper proposes four smoothing algorithms for head-driven parsing. Experiments indicate that these four smoothing algorithms have higher performance than the Baseline algorithm and reduce the disturbing curve of the optimized parameter significantly, which prove the effectiveness of the proposed approach.