Aiming at the problem that the text results of continuous sign language recognition based on video have problems of semantic ambiguity and chaotic word order, a two-step method was proposed to convert the sign language text of the continuous sign language recognition result into a fluent and understandable Chinese text. In the first step, the natural sign language rules and N-gram language model ( N-gram) were used to perform the text ordering of the continuous sign language recognition results. In the second step, a Bidirectional Long-Term Short-Term Memory (Bi-LSTM) network model was trained by using the Chinese universal quantifier dataset to solve the quantifier-free problem of the sign language grammar, so as to improve the fluency of texts. The absolute accuracy and the proportion of the longest correct subsequences were adopted as the evaluation indexes of text ordering. Experimental results showed that the text ordering results of the proposed method had the absolute accuracy of 77.06%, the proportion of the longest correct subsequences of 86.55%, and the accuracy of quantifier completion of 97.23%. The proposed method can effectively improve the smoothness and intelligibility of text results of continuous sign language recognition. It has been successfully applied to the video-based continuous sign language recognition, which improves the barrier-free communication experience between the hearing-impaired and the normal-hearing people.