Deep foundation pit is affected by several factors

and becomes the key problem in geotechnical engineering. The deformation must be controlled rigorously according to the actual situation surrounding environment and building safety grade. After analyzing the measured data of deformation

the prediction of deformation of supporting structure is made

and the safety of deep foundation pit is guranteed. To insure the construction quality of the deep foundation pit

deformation observation of the supporting structure is carried out in many deep pit in order to predict the development of deformation accurately during construction. A radial basis function (RBF) neural network learning algorithm based on immune recognition principle is studied. In the algorithm

the input data are regarded as antigens

and antibodies are regarded as the compression mapping of antigenas

i.e. the hidden layer centers. The weights of the output layer are determined by adopting the least square method

which can improve convergence speed and precision of the RBF neural network. A new time series prediction method based on artificial immune RBF neural network is also presented

and its application is discussed. The simulation experiment indicates that this method is effective in time series prediction.