1. A method of probabilistic error-tolerant natural language understanding, comprising: using a speech recognition to convert an utterance of a user into a possible word sequence set; dividing a concept grammar into a static grammar and a dynamic grammar, wherein the static grammar is predetermined and is not variable according to the input word sequence set, while the dynamic grammar is formed by comparing the input word sequence set and the static grammar; using the concept grammar to parse the word sequence set into a concept parse forest, wherein the concept parse forest further comprises at least one hypothetical concept sequence; using at least one exemplary concept sequence to represent the concept sequence which is well-formed being recognized by the concept grammar; and comparing the hypothetical concept sequences and the exemplary concept sequences to find the most possible concept sequence, and to convert the concept sequence into a semantic frame expressing intention of the user.