Currently， in reconstruction of normal speech from whispered speech based on neural network， the spectral envelope of the whisper is often used to estimate F₀ characteristics of the normal speech. Such algorithms have certain deficiencies in the accuracy of F₀. There is a clear lack of naturalness， and sometimes the pitch distortion occurs. This paper proposes a method for predicting the F₀ of normal speech frame by frame using the Bi-long short-term memory（BLSTM）deep network with the acoustic fusion feature of normal speech. Firstly， the STRAIGHT model and related codes are used to preprocess the whisper and the normal speech corpus. Respectively， extract the Mel-scale frequency cepstral coefficient（MFCC）， rhythm and spectral envelope of the whisper speech and the F₀ and spectral envelope of the normal speech. Secondly， the BLSTM deep network is used to establish a mapping relationship between spectrums of whisper and normal speech， and a mapping relationship between MFCC， rhythm and spectral envelope features of whisper speech and F₀ of normal speech. Finally， according to MFCC， rhythm and spectral envelope features of whisper speech，the F₀ and spectral envelope of the corresponding normal speech are obtained， and the normal speech is synthesized using the STRAIGHT model. The experimental results show that compared with the estimation of the F₀ using only the spectral envelope， the introduction of fusion features of phonetic rhythm and MFCC is a good complement to the F₀ features， which solves the phenomenon of pitch disorders and the converted speech is closer to normal speech in rhythm.