The invention relates to a number multi-dimensional
risk assessment method and
system based on
dynamic feature extraction, and the method comprises the steps: obtaining multi-source heterogeneous telecommunication abnormal data of a
client, carrying out the calculation power assessment, and carrying out the dynamic sampling according to a calculation power
score, and forming a local
training set;
feature extraction is carried out on the text data and the image data, and multi-
modal feature alignment and fusion are realized through a
generative adversarial network and an attention mechanism; in a
federated learning framework, dynamically adjusting a regularization coefficient according to the difference between a local model and a
global model by utilizing a
reinforcement learning model, and guiding a
client to carry out adaptive local training; the
server performs intelligent weighted aggregation on
client parameters based on
data quality and model similarity, and updates a
global model; and finally, a user heterogeneous graph is constructed based on the
global model and the multi-
modal features, neighbor selection is optimized through
reinforcement learning, and abnormal users are identified by using graph neural network aggregation information. According to the method and the device, on the premise of protecting data privacy, the
risk assessment accuracy of the number is effectively improved.