An open-source-license analyzing method performed by a server, comprising: receiving a file-to-be-tested and a planning condition ; detecting an open-source license of the file-to-be-tested ; matching the detected open-source license with the planning condition to determine a first conflict between the detected open-source license and the planning condition ; and generating a first risk assessment report based on the first conflict ; wherein the file-to-be-tested includes a plurality of documents for detection, and the detecting the open-source license of the file-to-be-tested comprises: for each document for detection, determining a vocabulary of the document for detection by using a k-shingle algorithm ; counting a word frequency of each word in the vocabulary in the document for detection, and determining a first characteristic matrix of the document for detection ; for an open-source license stored in a database, determining a word frequency of each word in the vocabulary in the open-source license, and determining a second characteristic matrix of the open-source license ; calculating a text similarity between the document for detection and the open-source license according to the first characteristic matrix and the second characteristic matrix ; and selecting the open-source license with a highest text similarity as the open-source license for the document for detection . receiving a file-to-be-tested and a planning condition ; detecting an open-source license of the file-to-be-tested ; matching the detected open-source license with the planning condition to determine a first conflict between the detected open-source license and the planning condition ; and generating a first risk assessment report based on the first conflict ; wherein the file-to-be-tested includes a plurality of documents for detection, and the detecting the open-source license of the file-to-be-tested comprises: for each document for detection, determining a vocabulary of the document for detection by using a k-shingle algorithm ; counting a word frequency of each word in the vocabulary in the document for detection, and determining a first characteristic matrix of the document for detection ; for an open-source license stored in a database, determining a word frequency of each word in the vocabulary in the open-source license, and determining a second characteristic matrix of the open-source license ; calculating a text similarity between the document for detection and the open-source license according to the first characteristic matrix and the second characteristic matrix ; and selecting the open-source license with a highest text similarity as the open-source license for the document for detection . for each document for detection, determining a vocabulary of the document for detection by using a k-shingle algorithm ; counting a word frequency of each word in the vocabulary in the document for detection, and determining a first characteristic matrix of the document for detection ; for an open-source license stored in a database, determining a word frequency of each word in the vocabulary in the open-source license, and determining a second characteristic matrix of the open-source license ; calculating a text similarity between the document for detection and the open-source license according to the first characteristic matrix and the second characteristic matrix ; and selecting the open-source license with a highest text similarity as the open-source license for the document for detection . <EOS>