1. A search method, comprising: receiving a query word string; retrieving a plurality of product information entries related to the query word string; extracting, from a memory, a first core product word from the query word string to obtain a first key product word, the first core product word being a smallest semantic unit; extracting, from the memory, a plurality of second core product words from the product information entries, the plurality of second core product words being smallest semantic units, wherein the extracting of the plurality of second core product words comprises: extracting last product words from the plurality of product information entries, the plurality of second core product words including the last product words, the plurality of product information entries corresponding to titles of products, descriptions of products, or a combination thereof; for a first second core product word, checking a list of candidate product words relating to the first key product word for a presence of the first second core product word; in the event that the first second core product word is present in the list of candidate product words, reducing a weighting of the product information entry corresponding to the first second core product word, wherein the list of candidate product words contains a first candidate product word, the first candidate product word relating to the first second core product word, wherein the first candidate product word is combined with the first key product word to obtain a synthesized product word, and wherein the first key product word and the synthesized product word do not belong to the same category; and sorting and outputting the product information entries according to the reduced weightings of the product information entries.
1. A search method, comprising: receiving a query word string; retrieving a plurality of product information entries related to the query word string; extracting, from a memory, a first core product word from the query word string to obtain a first key product word, the first core product word being a smallest semantic unit; extracting, from the memory, a plurality of second core product words from the product information entries, the plurality of second core product words being smallest semantic units, wherein the extracting of the plurality of second core product words comprises: extracting last product words from the plurality of product information entries, the plurality of second core product words including the last product words, the plurality of product information entries corresponding to titles of products, descriptions of products, or a combination thereof; for a first second core product word, checking a list of candidate product words relating to the first key product word for a presence of the first second core product word; in the event that the first second core product word is present in the list of candidate product words, reducing a weighting of the product information entry corresponding to the first second core product word, wherein the list of candidate product words contains a first candidate product word, the first candidate product word relating to the first second core product word, wherein the first candidate product word is combined with the first key product word to obtain a synthesized product word, and wherein the first key product word and the synthesized product word do not belong to the same category; and sorting and outputting the product information entries according to the reduced weightings of the product information entries. 2. The method as described in claim 1 , further comprising: establishing the list of candidate product words comprises: for at least one product information entry contained in a database: performing a coarse granularity segmentation by the largest semantic units; and extracting a third core product word contained in segmented results; determining whether the third core product word has been extracted from the segmented results; in the event that the third core product word has been extracted from the segmented results, performing a fine granularity segmentation by the smallest semantic units: determining whether at least two of the words obtained are product words; in the event that at least two of the words obtained are product words; using the first product word as a key product word; and using the last product word as a candidate product word of the key product word; computing correlations of at least one key product word and at least one candidate product word; determining whether the correlation of the at least one key product word and the at least one candidate product word meets a threshold value; selecting a candidate product word having a correlation that meets the threshold value; and for the same key product word, generating the list of candidate product words based on the selected candidate product word.