A network event prediction method, comprising: determining signaling sequence data of a user according to signaling plane data of the user, wherein the signaling plane data of the user comprises N pieces of signaling data and a timestamp of each of the N pieces of signaling data, each of the N pieces of signaling data comprises a signaling type, and N is a positive integer greater than or equal to 1; determining a first probability value of occurrence of a first network event according to the signaling sequence data of the user and a first supervised prediction model, wherein the first supervised prediction model represents a mapping relationship between the signaling sequence data of the user and a probability value of occurrence of the first network event; determining a second probability value of occurrence of the first network event according to expert feature data of the user and a second supervised prediction model, wherein the expert feature data of the user represents a value of at least one preset feature of the user, and the second supervised prediction model represents a mapping relationship between the expert feature data of the user and a probability value of occurrence of the first network event; and determining a predicted occurrence probability of the first network event according to the first probability value and the second probability value; wherein the determining signaling sequence data of a user according to signaling plane data of the user comprises: determining, according to the N pieces of signaling data of the user, N pieces of signaling that correspond one-to-one to the N pieces of signaling data; and determining the signaling sequence data of the user according to M preset signaling groups and the N pieces of signaling, wherein the signaling sequence data of the user represents whether the N pieces of signaling comprise m i pieces of preset signaling that are arranged in a time order, an i th preset signaling group of the M preset signaling groups comprises the m i pieces of preset signaling that are arranged in a time order, i is less than or equal to M and is a positive integer greater than or equal to 1, and m i is a positive integer greater than or equal to 1. determining signaling sequence data of a user according to signaling plane data of the user, wherein the signaling plane data of the user comprises N pieces of signaling data and a timestamp of each of the N pieces of signaling data, each of the N pieces of signaling data comprises a signaling type, and N is a positive integer greater than or equal to 1; determining a first probability value of occurrence of a first network event according to the signaling sequence data of the user and a first supervised prediction model, wherein the first supervised prediction model represents a mapping relationship between the signaling sequence data of the user and a probability value of occurrence of the first network event; determining a second probability value of occurrence of the first network event according to expert feature data of the user and a second supervised prediction model, wherein the expert feature data of the user represents a value of at least one preset feature of the user, and the second supervised prediction model represents a mapping relationship between the expert feature data of the user and a probability value of occurrence of the first network event; and determining a predicted occurrence probability of the first network event according to the first probability value and the second probability value; wherein the determining signaling sequence data of a user according to signaling plane data of the user comprises: determining, according to the N pieces of signaling data of the user, N pieces of signaling that correspond one-to-one to the N pieces of signaling data; and determining the signaling sequence data of the user according to M preset signaling groups and the N pieces of signaling, wherein the signaling sequence data of the user represents whether the N pieces of signaling comprise m i pieces of preset signaling that are arranged in a time order, an i th preset signaling group of the M preset signaling groups comprises the m i pieces of preset signaling that are arranged in a time order, i is less than or equal to M and is a positive integer greater than or equal to 1, and m i is a positive integer greater than or equal to 1. determining, according to the N pieces of signaling data of the user, N pieces of signaling that correspond one-to-one to the N pieces of signaling data; and determining the signaling sequence data of the user according to M preset signaling groups and the N pieces of signaling, wherein the signaling sequence data of the user represents whether the N pieces of signaling comprise m i pieces of preset signaling that are arranged in a time order, an i th preset signaling group of the M preset signaling groups comprises the m i pieces of preset signaling that are arranged in a time order, i is less than or equal to M and is a positive integer greater than or equal to 1, and m i is a positive integer greater than or equal to 1.