1. A method for identifying and extracting ideographic components, comprising: generating a bilingual sentence database after sentence alignment is performed on corpora of multilingual or bilingual text versions of same content; wherein different language texts are further associated by using ideographs; sentences of different language texts express same semantic content, the ideographs of different language texts are implemented by using four types of common ideographic components, and the four types of ideographic components are identified and extracted as follows; S1. reading a sentence and matching a frame ( 102 ); a sentence includes two parts: a sentence frame and a sentence cabin; in each round of identifying and extracting ideographic components, texts of languages A and B are selected; a machine reads a bilingual sentence pair of languages A and B from the bilingual sentence database, inquires a sentence frame database by using the current bilingual sentence pair to perform a sentence frame match operation, integrates the bilingual sentence pair into a matched sentence frame, and displays “Cabin Detection” and “Search, Match, and Label” buttons; after integration, if each sentence cabin is uneven or incomplete or stacking exists, and the “Search, Match, and Label” button is clicked, the machine performs a “search, match, and label” operation, and uses a word string of the sentence of language A to inquire a conventional electronic dictionary and generate a semantic match table for the current sentence pair according to whether the sentence of language B includes a word string corresponding to semantic content; then the cabin extracts a new sentence frame; S2. identifying and labeling sentence cabins ( 103 ); the machine simultaneously identifies bilingual quantity strings, proper noun strings, and article noun strings sequentially, and pre-labels them as sentence cabins; the machine checks the sentence frame format and collates the foregoing pre-labeled sentence cabins as formally labeled sentence cabins; this is a step of identification and extraction in the first round; parts of speech do not need to be identified in identification and extraction starting from a second round; instead, labeled language sentences are used as a template to identify and label sentence cabins of unlabeled language sentences; S3. detecting cabins and extracting a cabin model ( 106 ); the machine detects sentence cabins one by one; in the first round of identification and extraction, the machine counts word strings included in sentence cabins of a sentence of a Pinyin text of language A; if the quantity of word strings included in a sentence cabin is greater than or equal to five original word strings, the machine needs to perform an operation of composing complex words; if the quantity of word strings included in a sentence cabin is greater than or equal to eight original word strings, the machine performs a “search, match, and label” operation by using the current sentence cabin, and generates a semantic match table for the current sentence cabin, so that a cabin model is further extracted as a sentence cabin with a model; the machine extracts the cabin model, and simultaneously identifies bilingual quantity strings, proper noun strings, and article noun strings sequentially, and pre-labels them as cabin eyes; a model example percentage is decreased from 100% gradually; after pre-labeling is completed, if the quantity of word strings of the three types is still greater than 70%, the machine labels zero-article noun strings or other semantic match word strings as cabin eyes under a prerequisite that spacing exists, accepts the's approval or modification or addition of a semantic match word string by using a sense group alignment operation, and continues to pre-label cabin eyes; when conditions for pre-labeling do not exist and the model example percentage is between 50% and 70%, the operation of pre-labeling cabin eyes ends; the pre-labeled cabin eyes are modified as formally labeled according to a left-to-right order of English sentence cabins; differently, in extraction starting from the second round, labeled language sentence cabins are used as a template to identify and label unlabeled language cabin eyes; when the cabin eye quantity is greater than or equal to 1, residual parts after cabin eyes are removed from the current sentence cabin are cabin model components, and they are saved to corresponding language cabin model fields in a cabin model database ( 107 ); and then the machine continues to detect other sentence cabins, until detection of sentence cabins of the whole sentence pair is completed; S4. receiving and storing sense group strings ( 108 ); the machine integrates sentence cabins with cabin models back into the corresponding cabin models, and then searches for the sentence cabins or cabin eyes one by one; with reference to the semantic match table, if word fields of language A and word fields of language B are not empty in records of the match table of the corresponding content and semantic content of texts of languages A and B is aligned, the content is determined to be sense group string components, and fetched from the records in succession, and respectively stored in corresponding language fields in a same record in a sense group database ( 109 ); and then the operation of identifying and extracting the current sentence pair ends.