A method of evaluating a domain name, comprising: retrieving a raw domain name and dividing the raw domain name into a plurality of parts; retrieving a specific part of the parts, wherein the specific part comprises at least one character (612a, 612b, 612c, 612d, 612e, 612f); encoding the at least one character (612a, 612b, 612c, 612d, 612e, 612f) into at least one encoded data (613a, 613b, 613c, 613d, 613e, 613f), wherein each of the encoded data (613a, 613b, 613c, 613d, 613e, 613f) is a number; padding the at least one encoded data (613a, 613b, 613c, 613d, 613e, 613f) to a specific length, comprising: calculating a difference between a length of the at least one encoded data (613a, 613b, 613c, 613d, 613e, 613f) and the specific length; prefixing the at least one encoded data (613a, 613b, 613c, 613d, 613e, 613f) with at least one specific number, wherein a number of the at least one specific number is equal to the difference; projecting the encoded data being padded to a plurality of embedded vectors (X 1 -X 75), wherein the at least one encoded data being padded one-to-one corresponds to the embedded vectors (X 1 -X 75); sequentially inputting the embedded vectors (X 1 -X 75) to a plurality of cells (C 1 -C 75) of a long short term memory model to generate a result vector; and converting the result vector to a prediction probability via a fully-connected layer and a specific function, wherein the specific function is a Sigmoid function, and the step of converting the result vector to the prediction probability via a fully-connected layer and the specific function comprises: mapping the result vector to a reference value through the fully-connected layer; and converting the reference value to the prediction probability by the Sigmoid function, wherein the prediction probability represents a probability that the raw domain name is generated by a domain generation algorithm. retrieving a raw domain name and dividing the raw domain name into a plurality of parts; retrieving a specific part of the parts, wherein the specific part comprises at least one character (612a, 612b, 612c, 612d, 612e, 612f); encoding the at least one character (612a, 612b, 612c, 612d, 612e, 612f) into at least one encoded data (613a, 613b, 613c, 613d, 613e, 613f), wherein each of the encoded data (613a, 613b, 613c, 613d, 613e, 613f) is a number; padding the at least one encoded data (613a, 613b, 613c, 613d, 613e, 613f) to a specific length, comprising: calculating a difference between a length of the at least one encoded data (613a, 613b, 613c, 613d, 613e, 613f) and the specific length; prefixing the at least one encoded data (613a, 613b, 613c, 613d, 613e, 613f) with at least one specific number, wherein a number of the at least one specific number is equal to the difference; calculating a difference between a length of the at least one encoded data (613a, 613b, 613c, 613d, 613e, 613f) and the specific length; prefixing the at least one encoded data (613a, 613b, 613c, 613d, 613e, 613f) with at least one specific number, wherein a number of the at least one specific number is equal to the difference; projecting the encoded data being padded to a plurality of embedded vectors (X 1 -X 75), wherein the at least one encoded data being padded one-to-one corresponds to the embedded vectors (X 1 -X 75); sequentially inputting the embedded vectors (X 1 -X 75) to a plurality of cells (C 1 -C 75) of a long short term memory model to generate a result vector; and converting the result vector to a prediction probability via a fully-connected layer and a specific function, wherein the specific function is a Sigmoid function, and the step of converting the result vector to the prediction probability via a fully-connected layer and the specific function comprises: mapping the result vector to a reference value through the fully-connected layer; and converting the reference value to the prediction probability by the Sigmoid function, wherein the prediction probability represents a probability that the raw domain name is generated by a domain generation algorithm. mapping the result vector to a reference value through the fully-connected layer; and converting the reference value to the prediction probability by the Sigmoid function, wherein the prediction probability represents a probability that the raw domain name is generated by a domain generation algorithm.