Aiming at the problem of large amount of the steering gear test data and unbalanced samples, an anomaly detection model is proposed that uses the Grey Wolf Optimization (GWO) to optimize the Deep Neural Networks (DNN) and combines it with the Logistic Regression Classification (GWO-DNN-LRC). The construction of the model effectively solves the problem that small samples in the steering gear test data are difficult to be accurately classified, and is suitable for the deep feature extraction and multi-fault classification of the steering gear test data. The accuracy of this method reaches 99.261%, which is 4.931%, 0.205%, and 0.087% higher than LRC, DNN, and GWO-DNN, respectively. The precision, recall, and F-score reach 98.417%, 98.062%, and 98.217%. In the comparison of classification accuracy of different categories, the categories of 6 small samples can reach 100%. Experimental results show that this method fully improves the performance of anomaly detection of steering gear, and is an effective application of deep learning technology in steering gear test data.