The invention discloses a flow wearing characteristic prediction and service life evaluation method based on ELM neural network model. The method comprises the steps of acquiring training sample data, acquiring testing sample data, establishing the ELM neural network model, and performing flow wearing characteristic prediction and service life evaluation by means of the constructed ELM neural network model. The method realizes prediction for a pressure bearing tube bank system flow wearing characteristic and service life evaluation. The method aims at a flow wearing problem of process type industrial pressure bearing tube bank system such as petrochemical engineering and coal chemical engineering. A quick quantitative prediction and residual service life evaluation method is established based on the ELM neural network model. The method can quickly and quantitatively predicate the flow wearing rate in a complicated variable-condition environment, and can supply scientific guidance for safe closed-loop management such as on-duty testing, risk evaluation, service life prediction and control optimization of the pressure bearing tube bank system, thereby realizing safe, stable and long-period operation of a flow wearing high-risk equipment system.