A method, comprising: in response to a human or machine identification request sent by a client, sending information pertaining to a first operation configured to identify human or machine to the client to be displayed; receiving a second operation that is submitted by the client, the second operation being triggered in response to an interactive operation performed on an operation track associated with the first operation; and identifying a triggering entity for the second operation as either a human or a machine based on a behavior characteristic model, wherein: the behavior characteristic model includes a classifier for human operations and machine operations; the classifier is trained based on operation samples; and the identifying of the triggering entity comprises: extracting behavioral characteristic data of the second operation, comprising: extracting first behavioral characteristic data of the second operation; pre-filtering the first behavioral characteristic data of the second operation based on an operation movement speed and a degree of operation randomness; and reducing, using a principal component analysis technique, dimensions of the pre-filtered first behavioral characteristic data to obtain the behavioral characteristic data of the second operation; and classifying the extracted behavioral characteristic data as either a human or a machine by inputting the extracted behavior characteristic data into the behavior characteristic model. in response to a human or machine identification request sent by a client, sending information pertaining to a first operation configured to identify human or machine to the client to be displayed; receiving a second operation that is submitted by the client, the second operation being triggered in response to an interactive operation performed on an operation track associated with the first operation; and identifying a triggering entity for the second operation as either a human or a machine based on a behavior characteristic model, wherein: the behavior characteristic model includes a classifier for human operations and machine operations; the classifier is trained based on operation samples; and the identifying of the triggering entity comprises: extracting behavioral characteristic data of the second operation, comprising: extracting first behavioral characteristic data of the second operation; pre-filtering the first behavioral characteristic data of the second operation based on an operation movement speed and a degree of operation randomness; and reducing, using a principal component analysis technique, dimensions of the pre-filtered first behavioral characteristic data to obtain the behavioral characteristic data of the second operation; and classifying the extracted behavioral characteristic data as either a human or a machine by inputting the extracted behavior characteristic data into the behavior characteristic model. the behavior characteristic model includes a classifier for human operations and machine operations; the classifier is trained based on operation samples; and the identifying of the triggering entity comprises: extracting behavioral characteristic data of the second operation, comprising: extracting first behavioral characteristic data of the second operation; pre-filtering the first behavioral characteristic data of the second operation based on an operation movement speed and a degree of operation randomness; and reducing, using a principal component analysis technique, dimensions of the pre-filtered first behavioral characteristic data to obtain the behavioral characteristic data of the second operation; and classifying the extracted behavioral characteristic data as either a human or a machine by inputting the extracted behavior characteristic data into the behavior characteristic model. extracting behavioral characteristic data of the second operation, comprising: extracting first behavioral characteristic data of the second operation; pre-filtering the first behavioral characteristic data of the second operation based on an operation movement speed and a degree of operation randomness; and reducing, using a principal component analysis technique, dimensions of the pre-filtered first behavioral characteristic data to obtain the behavioral characteristic data of the second operation; and extracting first behavioral characteristic data of the second operation; pre-filtering the first behavioral characteristic data of the second operation based on an operation movement speed and a degree of operation randomness; and reducing, using a principal component analysis technique, dimensions of the pre-filtered first behavioral characteristic data to obtain the behavioral characteristic data of the second operation; and classifying the extracted behavioral characteristic data as either a human or a machine by inputting the extracted behavior characteristic data into the behavior characteristic model.