With the wide application of computer technology, medical health data has also increased dramatically, and data-driven medical big data analysis methods have emerged as the times require, providing assistance for intelligent identification of medical health. However, due to the mixed medical big data format, many incomplete records, and a lot of noise, it is still difficult to analyze medical big data. Traditional machine learning methods can't effectively mine the rich information contained in medical big data, while deep learning builds a hierarchical model by simulating the human brain. It has powerful automatic feature extraction, complex model construction and efficient feature expression, and more important. It is a deep learning method that extracts features from the bottom to the top level from the original medical image data. Therefore, this paper constructs a data analysis model based on deep learning for medical images and transcripts, and is used for intelligent identification and diagnosis of diseases. The model uses massive medical big data to select and optimize model parameters, and automatically learns the pathological analysis process of doctors or medical researchers through the model, and finally intelligently conducts disease judgment and effective decision based on the analysis results of medical big data. The experimental results show that the method can analyze the medical big data, and can realize the early diagnosis of the disease. At the same time, it can analyze the physical health status according to the patient's physical examination records and predict the risk of a certain disease in the future. Greatly reduce the work pressure of doctors or medical researchers and improve their work efficiency.INDEX TERMS Medical big data, analysis, deep learning, intelligent recognition.

To identify plant electrical signals effectively, a new feature extraction method based on multiwavelet entropy and principal component analysis is proposed. The wavelet energy entropy, wavelet singular entropy, and the wavelet variance entropy of plants’ electrical signals are extracted by a wavelet transformation to construct the combined features. Principal component analysis (PCA) is applied to treat the constructed features and eliminate redundant information among those features and extract features which can reflect signal type. Finally, the classification method of BP neural network is used to classify the obtained feature vectors. The experimental results show that this method can acquire comparatively high recognition rate, which proposed a new efficient solution for the identification of plant electrical signals.

In order to make the manipulator track the desired trajectory in a finite time, a new control method based on fast nonsingular terminal sliding mode has been designed. This method combines the traditional fast terminal sliding mode with the nonsingular terminal sliding mode, has rapidity, nonsingularity, finite-time convergence, and strong robustness, and can effectively suppress the inherent chattering phenomenon of the sliding mode controller. The nonsingular fast terminal sliding mode surface is used to accelerate the convergence speed of manipulator trajectory tracking error, and the singularity problem in terminal sliding mode is solved. The finite-time convergence of the algorithm is proved by the Lyapunov function. The simulation results demonstrate that the proposed method can achieve accurate finite-time trajectory tracking characteristics and has robustness against external disturbances.