您所指的称为多输入模型，可以在大多数 deel 学习框架中轻松构建。这个想法是将两种类型的数据作为单独的输入，然后根据它们的类型使用特定的层（循环层对数据进行排序，CNN 对图像，等等），然后将它们连接在一起。

如果您可以使用 Keras，那么功能 Api非常适合手头的任务。您的问题的代码示例（基于文档中给出的示例）可能是：

from keras.layers import Input, Embedding, LSTM, Dense, merge
from keras.models import Model

# headline input: meant to receive sequences of 100 integers, between 1 and 10000.
# note that we can name any layer by passing it a "name" argument.
main_input = Input(shape=(100,), dtype='int32', name='main_input')

# this embedding layer will encode the input sequence
# into a sequence of dense 512-dimensional vectors.
x = Embedding(output_dim=512, input_dim=10000, input_length=100)(main_input)

# a LSTM will transform the vector sequence into a single vector,
# containing information about the entire sequence
lstm_out = LSTM(32)(x)

#At this point, we feed into the model our auxiliary input data by concatenating it with the LSTM output:

auxiliary_input = Input(shape=(5,), name='aux_input')
x = merge([lstm_out, auxiliary_input], mode='concat')

# we stack a deep fully-connected network on top
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)

# and finally we add the main logistic regression layer
main_output = Dense(1, activation='sigmoid', name='main_output')(x)

#This defines a model with two inputss:
model = Model(input=[main_input, auxiliary_input], output=main_output)

#Then compite and train
model.compile(optimizer='rmsprop', loss='binary_crossentropy')
model.fit([headline_data, additional_data], labels,
          nb_epoch=50, batch_size=32)

在您的情况下，动态数据将是 ，headline_input而您的静态数据是auxiliary_input. 该模型将两者都采用，将循环层应用于前者并将它们连接起来以通过密集层传递联合。

当然，其中许多参数将取决于您的数据，但至少这个示例将让您了解如何构建此类模型。

还有一个有趣的项目conditionall RNN也是为此目的。值得一看。