Finding robust solutions to time series classification problems using deep neural networks has received wide attention. However, unreliable data makes classification very difficult. Traditional deep neural networks cannot effectively solve problems with strong noise. In this paper, we propose a hybrid convolutional neural network (CNN) model combined with a genetic algorithm (GA) for time series classification (TSC) with unreliable data. To obtain a robust CNN structure, even though network structural optimization is an NP-hard problem, we design a GA for network structure optimization. Several benchmarks and actual datasets are adopted, and tests are carried out to prove the effectiveness of the proposed GA-based CNN. The numerical results show that our approach has better performance than other state-of-the-art deep neural networks.