The section constrains could make an impact on the calculating efficiency and results of the spot market clearing. At present, the pre-warning methods of transmission capacity constraints generally focus on deterministic scenarios through artificial experience. The operation mode of power system could be more complicated under spot market. To enhance the compatibility and comprehensiveness of section constrains in spot market clearing, this paper proposes a pre-warning idea of transmission capacity constraints based on Monte Carlo method to handle the uncertainty of the prediction error from renewable energy, load, and power plant bidding, which can effectively evaluate the safety margin/risk degree of transmission capacity constraints. On this basis, considering that Monte Carlo method needs to solve a large number of samples, which the computational burden is huge, this paper further proposes a fast algorithm for transmission capacity constraints pre-warning based on deep learning technology. The algorithm directly solves all the samples generated by Monte Carlo by function mapping. The samples can be calculated by the proposed algorithm in seconds. Finally, the effectiveness of the proposed method is verified in the IEEE 118 standard test system with renewable energy sources.